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SP800-90 series on Random Number Generation, NIST; Random Number Generation in the GNU Scientific Library Reference Manual; Random Number Generation Routines in the NAG Numerical Library; Chris Lomont's overview of PRNGs, including a good implementation of the WELL512 algorithm; Source code to read data from a TrueRNG V2 hardware TRNG
A random password generator is a software program or hardware device that takes input from a random or pseudo-random number generator and automatically generates a password. Random passwords can be generated manually, using simple sources of randomness such as dice or coins , or they can be generated using a computer.
var x1 = 0; // A global variable, because it is not in any function let x2 = 0; // Also global, this time because it is not in any block function f {var z = 'foxes', r = 'birds'; // 2 local variables m = 'fish'; // global, because it wasn't declared anywhere before function child {var r = 'monkeys'; // This variable is local and does not affect the "birds" r of the parent function. z ...
Xorshift random number generators, also called shift-register generators, are a class of pseudorandom number generators that were invented by George Marsaglia. [1] They are a subset of linear-feedback shift registers (LFSRs) which allow a particularly efficient implementation in software without the excessive use of sparse polynomials. [2]
Copy-and-paste programming, sometimes referred to as just pasting, is the production of highly repetitive computer programming code, as produced by copy and paste operations. It is primarily a pejorative term; those who use the term are often implying a lack of programming competence and ability to create abstractions.
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push the constant 0.0 (a double) onto the stack dconst_1 0f 0000 1111 → 1.0 push the constant 1.0 (a double) onto the stack ddiv 6f 0110 1111 value1, value2 → result divide two doubles dload 18 0001 1000 1: index → value load a double value from a local variable #index: dload_0 26 0010 0110 → value load a double from local variable 0 ...
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...