LYCOS RETRIEVER 
Pseudorandom Number Generator
built 783 days ago
This class defines a Pseudo-random number generator that precisely matches the behavior of the MSVCRT 6.0 random routines. That is to say, for equivalent calls to ::srand() and Random::srand(), both ::rand() and Random::rand() will produce the same results.
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This is a collection of selected linear pseudorandom number that were implemented in commercial software, used in applications, and some of which have extensively been tested. The quality of these generators is examined using scatter plots and the spectral test. In addition, the spectral test is applied to study the applicability of linear congruential generators on parallel architectures.
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Even on an implementation with a theoretically perfect random number generator (for example, based on hashing Brownian motion), a good player may survive over 150 consecutive S and Z tetrominoes. The probability at any given time of the next 150 tetrominoes being only S and Z is one in (7/2)150 (approximately one in 4 × 1081). This number has the same order of magnitude as the number of atoms in the known universe.[5]
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Generates the next pseudorandom number. This returns a value between 0(inclusive) and n(exclusive), and each value has the same likelihodd (1/n). (0 and 1 are equally likely). The implementation for java.util.Random is:
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One of the tricks in designing physical random-number generators such as Random.org or LavaRnd is to erase any bias in the numbers. In a truly random stream of numbers, each combination of digits should appear as often as any other combination. However, in numbers generated from the physical world, this is rarely the case. Even a coin toss, the seeming epitome of randomness, is subtly biased. Earlier this year, mathematicians proved that a tossed coin is slightly more likely to land on the face it started out on than on the opposite face (SN: 2/28/04, p. 131: http://www.sciencenews.org/articles/20040228/fob2.asp).
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Random and pseudorandom numbers are needed for many cryptographic applications. For example, common cryptosystems employ keys that must be generated in a random fashion. Many cryptographic protocols ... require random or pseudorandom inputs at various points, e.g., for auxiliary quantities used in generating digital signatures or for generating challenges in authentication protocols.
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Pseudorandom Number Generator