MINSTD Shuffle

Create a readable stream for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

Installation

To use as a general utility, install the CLI package globally

npm install -g @stdlib/random-streams-minstd-shuffle-cli

Usage

Usage: random-minstd-shuffle [options]

Options:

  -h,  --help               Print this message.
  -V,  --version            Print the package version.
       --sep sep            Separator used to join streamed data. Default: '\n'.
  -n,  --iter iterations    Number of pseudorandom numbers.
       --normalized         Generate pseudorandom numbers on the interval [0,1).
       --seed seed          Pseudorandom number generator seed.
       --state filepath     Path to a file containing the pseudorandom number
                            generator state.
       --snapshot filepath  Output file path for saving the pseudorandom number
                            generator state upon exit.

Notes

• In accordance with POSIX convention, a trailing newline is always appended to generated output prior to exit.
• Specifying a "snapshot" file path is useful when wanting to resume pseudorandom number generation due to, e.g., a downstream failure in an analysis pipeline. Before exiting, the process will store the pseudorandom number generator state in a file specified according to a provided file path. Upon loading a snapshot (state), the process will generate pseudorandom numbers starting from the loaded state, thus avoiding having to seed and replay an entire analysis.

Examples

$ random-minstd-shuffle -n 10 --seed 1234

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.

See Also

• @stdlib/random-streams-minstd-shuffle: create a readable stream for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• @stdlib/random-base/minstd-shuffle: A linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• @stdlib/random-iter/minstd-shuffle: create an iterator for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• @stdlib/random-streams/minstd: create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• @stdlib/random-streams/mt19937: create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator.
• @stdlib/random-streams/randi: create a readable stream for generating pseudorandom numbers having integer values.
• @stdlib/random-streams/randu: create a readable stream for generating uniformly distributed pseudorandom numbers between 0 and 1.

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

License

See LICENSE.

Copyright

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