Pseudo-random number generator based on linear congruence and delayed Fibonacci method
Pseudo-random number generator based on linear congruence and delayed Fibonacci method
Radosław Cybulski
a:1:{s:5:"en_US";s:87:"Faculty of Mathematics and Computer Science, Univeristy of Warmia and Mazury in Olsztyn";}Abstrakt
Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators – a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.
Słowa kluczowe:
Linear congruential method, Delayed Fibonacci technique, Hybrid pseudo-random number generatorBibliografia
AHRENS J.H., DIETER U. 1988. Efficient table – free sampling methods for the exponential, Cauchy, and normal distributions. Communication of the ACM, 31(11): 1330-1337. Google Scholar
BELL J.R. 1968. Algorithm 334: Normal random deviates. Communication of the ACM, 11(7): 498. Google Scholar
BOX G.E.P., MULLER M.E. 1958. A note on the generation of random normal deviates. Annals of Mathematical Statistics, 29(2): 610-611. Google Scholar
ICS-a. Donald Bren School of Information and Computer Sciences. University of California, Irvine. https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/australian/australian.dat. Google Scholar
ICS-b. Donald Bren School of Information and Computer Sciences. University of California, Irvine. https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/heart/heart.dat. Google Scholar
KINDERMAN A.J., MONAHAN J.F. 1977. Computer generation of random variables using the ratio of uniform deviates. ACM Transactions on Mathematical Software, 3(3): 257-260. Google Scholar
KNOP R. 1969. Remark on Algorithm 334 [g5]: normal random deviates. McGill University, Montreal. Google Scholar
Lagged Fibonacci Generator. Security and So Many Things, Asecuritysite. https://asecuritysite.com/encryption/fab. Google Scholar
RUKHIN A., SOTO J., NECHVATAL J., SMID M., BARKER E., LEIGH S., LEVENSON M., VANGEL M., BANKS D., HECKERT A., DRAY J., VO S. 2001. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD. Google Scholar
STALLINGS W. 2012. Kryptografia i bezpieczeństwo sieci komputerowych – matematyka szyfrów i techniki kryptologii. Helion, Gliwice. Google Scholar
SULEWSKI P. 2019. Porównanie generatorów liczb pseudolosowych. Wiadomości Statystyczne, 7: 5-31. Google Scholar
WIECZORKOWSKI R., ZIELINSKI R. 1997. Komputerowe generatory liczb losowych. Wydawnictwo Naukowo-Techniczne, Warszawa. Google Scholar
WOJTATOWICZ T.W. 1998. Metody analizy danych doświadczalnych. Wydawnictwo Politechniki Łódzkiej, Łódź. Google Scholar
a:1:{s:5:"en_US";s:87:"Faculty of Mathematics and Computer Science, Univeristy of Warmia and Mazury in Olsztyn";}