NAME rand - subtractive 100 shuffle pseudo-random number generator SYNOPSIS rand([[min, ] beyond]) TYPES min integer beyond integer return integer DESCRIPTION Generate a pseudo-random number using an subtractive 100 shuffle generator. We return a pseudo-random number over the half closed interval: [min,beyond) ((min <= return < beyond)) By default, min is 0 and beyond is 2^64. The shuffle method is fast and serves as a fairly good standard pseudo-random generator. If you need a fast generator and do not need a cryptographically strong one, this generator is likely to do the job. Casual direct use of the shuffle generator may be acceptable. For a much higher quality cryptographically strong (but slower) generator use the Blum-Blum-Shub generator (see the random help page). Other arg forms: rand() Same as rand(0, 2^64) rand(beyond) Same as rand(0, beyond) The rand generator generates the highest order bit first. Thus: rand(256) will produce the save value as: (rand(8) << 5) + rand(32) when seeded with the same seed. The rand generator has two distinct parts, the subtractive 100 method and the shuffle method. The subtractive 100 method is described in: "The Art of Computer Programming - Seminumerical Algorithms", Vol 2, 3rd edition (1998), Section 3.6, page 186, formula (2). The "use only the first 100 our of every 1009" is described in Knuth's "The Art of Computer Programming - Seminumerical Algorithms", Vol 2, 3rd edition (1998), Section 3.6, page 188". The period and other properties of the subtractive 100 method make it very useful to 'seed' other generators. The shuffle method is feed values by the subtractive 100 method. The shuffle method is described in: "The Art of Computer Programming - Seminumerical Algorithms", Vol 2, 3rd edition (1998), Section 3.2.2, page 34, Algorithm B. The rand generator has a good period, and is fast. It is reasonable as generators go, though there are better ones available. The shuffle method has a very good period, and is fast. It is fairly good as generators go, particularly when it is feed reasonably random numbers. Because of this, we use feed values from the subtractive 100 method into the shuffle method. The rand generator uses two internal tables: additive table - 100 entries of 64 bits used by the subtractive 100 method shuffle table - 256 entries of 64 bits used by the shuffle method feed by the subtractive 100 method from the subtractive table The goals of this generator are: * all magic numbers are explained I (Landon Curt Noll) distrust systems with constants (magic numbers) and tables that have no justification (e.g., DES). I believe that I have done my best to justify all of the magic numbers used. * full documentation You have this source file, plus background publications, what more could you ask? * large selection of seeds Seeds are not limited to a small number of bits. A seed may be of any size. Most of the magic constants used by this generator ultimately are based on the Rand book of random numbers. The Rand book contains 10^6 decimal digits, generated by a physical process. This book, produced by the Rand corporation in the 1950's is considered a standard against which other generators may be measured. The Rand book of numbers was groups into groups of 20 digits. The first 100 groups < 2^64 were used to initialize the default additive table. The size of 20 digits was used because 2^64 is 20 digits long. The restriction of < 2^64 was used to prevent modulus biasing. The shuffle table size is longer than the 100 entries recommended by Knuth. We use a power of 2 shuffle table length so that the shuffle process can select a table entry from a new subtractive 100 value by extracting its low order bits. The value 256 is convenient in that it is the size of a byte which allows for easy extraction. We use the upper byte of the subtractive 100 value to select the shuffle table entry because it allows all of 64 bits to play a part in the entry selection. If we were to select a lower 8 bits in the 64 bit value, carries that propagate above our 8 bits would not impact the subtractive 100 generator output. It is 'nice' when a seed of "n" produces a 'significantly different' sequence than a seed of "n+1". Generators, by convention, assign special significance to the seed of '0'. It is an unfortunate that people often pick small seed values, particularly when large seed are of significance to the generators found in this file. An internal process called randreseed64 will effectively eliminate the human perceptions that are noted above. It should be noted that the purpose of randreseed64 is to scramble a seed ONLY. We do not care if these generators produce good random numbers. We only want to help eliminate the human factors & perceptions noted above. The randreseed64 process scrambles all 64 bit chunks of a seed, by mapping [0,2^64) into [0,2^64). This map is one-to-one and onto. Mapping is performed using a linear congruence generator of the form: X1 <-- (a*X0 + c) % m with the exception that: 0 ==> 0 (so that srand(0) acts as default) while maintaining a 1-to-1 and onto map. The randreseed64 constants 'a' and 'c' based on the linear congruential generators found in: "The Art of Computer Programming - Seminumerical Algorithms" by Knuth, Vol 2, 2nd edition (1981), Section 3.6, pages 170-171. We will select the randreseed64 multiplier 'a' such that: a mod 8 == 5 (based on note iii) 0.01*m < a < 0.99*m (based on note iv) 0.01*2^64 < a < 0.99*2^64 a is prime (help keep the generators independent) The choice of the randreseed64 adder 'c' is considered immaterial according (based in note v). Knuth suggests 'c==1' or 'c==a'. We elect to select 'c' using the same process as we used to select 'a'. The choice is 'immaterial' after all, and as long as: gcd(c, m) == 1 (based on note v) gcd(c, 2^64) == 1 gcd(a, c) == 1 (adders & multipliers will be more independent) The values 'a' and 'c for randreseed64 are taken from the Rand book of numbers. Because m=2^64 is 20 decimal digits long, we will search the Rand book of numbers 20 at a time. We will skip any of the 100 values that were used to initialize the subtractive 100 generators. The values obtained from the Rand book are: a = 6316878969928993981 c = 1363042948800878693 As we stated before, we must map 0 ==> 0 so that srand(0) does the default thing. The randreseed64 would normally map as follows: 0 ==> 1363042948800878693 (0 ==> c) To overcome this, and preserve the 1-to-1 and onto map, we force: 0 ==> 0 10239951819489363767 ==> 1363042948800878693 One might object to the complexity of the seed scramble/mapping via the randreseed64 process. But Calling srand(0) with the randreseed64 process would be the same as calling srand(10239951819489363767) without it. No extra security is gained or reduced by using the randreseed64 process. The meaning of seeds are exchanged, but not lost or favored (used by more than one input seed). The randreseed64 process does not reduce the security of the rand generator. Every seed is converted into a different unique seed. No seed is ignored or favored. The truly paranoid might suggest that my claims in the MAGIC NUMBERS section are a lie intended to entrap people. Well they are not, but if you that paranoid why would you use a non-cryptographically strong pseudo-random number generator in the first place? You would be better off using the random() builtin function. The two constants that were picked from the Rand Book of Random Numbers The random numbers from the Rand Book of Random Numbers can be verified by anyone who obtains the book. As these numbers were created before I (Landon Curt Noll) was born (you can look up my birth record if you want), I claim to have no possible influence on their generation. There is a very slight chance that the electronic copy of the Rand Book of Random Numbers that I was given access to differs from the printed text. I am willing to provide access to this electronic copy should anyone wants to compare it to the printed text. When using the s100 generator, one may select your own 100 subtractive values by calling: srand(mat100) and avoid using my magic numbers. The randreseed64 process is NOT applied to the matrix values. Of course, you must pick good subtractive 100 values yourself! EXAMPLE ; print srand(0), rand(), rand(), rand() RAND state 2298441576805697181 3498508396312845423 5031615567549397476 ; print rand(123), rand(123), rand(123), rand(123), rand(123), rand(123) 106 59 99 99 25 88 ; print rand(2,12), rand(2^50,3^50), rand(0,2), rand(-400000, 120000) 2 658186291252503497642116 1 -324097 LIMITS min < beyond LINK LIBRARY void zrand(long cnt, ZVALUE *res) void zrandrange(ZVALUE low, ZVALUE beyond, ZVALUE *res) long irand(long beyond) SEE ALSO seed, srand, randbit, isrand, random, srandom, israndom ## Copyright (C) 1999-2007,2021 Landon Curt Noll ## ## Calc is open software; you can redistribute it and/or modify it under ## the terms of the version 2.1 of the GNU Lesser General Public License ## as published by the Free Software Foundation. ## ## Calc is distributed in the hope that it will be useful, but WITHOUT ## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ## or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General ## Public License for more details. ## ## A copy of version 2.1 of the GNU Lesser General Public License is ## distributed with calc under the filename COPYING-LGPL. You should have ## received a copy with calc; if not, write to Free Software Foundation, Inc. ## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## ## Under source code control: 1996/01/01 02:16:09 ## File existed as early as: 1996 ## ## chongo /\oo/\ http://www.isthe.com/chongo/ ## Share and enjoy! :-) http://www.isthe.com/chongo/tech/comp/calc/