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#Random generator algorithm full
#Random generator algorithm software
#Random generator algorithm code

13 th April, 2008: Revised article to explain why this generator might be preferable to the built-in generator.

If you are using C++, see Random number generation using C++ TR1. Further Readingįor more information on random number generation, particularly on subtle things that can go wrong, see the CodeProject article Pitfalls in Random Number Generation.

#Random generator algorithm code

The test code also uses RunningStat, a class for accurately computing sample mean and variance as values accumulate. The test is good enough to catch most coding errors since a bug would likely result in the test failing far more often. Try again with another seed and it will most likely pass.

#Random generator algorithm software

This is highly unusual in software testing: the test should fail occasionally! That's statistics for you. If this test were applied repeatedly with ideal random input, the test would fail on average once in every thousand applications. The test code included as a demo uses a statistical test, the Kolmogorov-Smirnov test, to confirm that the output of the generator has the expected statistical properties. The code to test SimpleRNG is more complicated than SimpleRNG itself. Once the class is initialized, there is only one public method to call, GetUniform(). There is also an option to set the seed values from the system clock using SetSeedFromSystemTime(). Some may prefer to throw an exception in this case rather than silently fix the problem. These arguments must be non-zero if an argument is zero, it is replaced by the default value.

These have default values, or they can be specified by calling SetSeed() with one or two arguments. The result is strictly between 0 and 1. The magic number below is 1/(2^32 + 2). Here is the method that generates uniformly distributed unsigned integers.Ĭopy Code public static double GetUniform() The heart of SimpleRNG is three lines of code. The algorithm passes Marsaglia's DIEHARD battery of tests, the acid test suite for random number generators. The algorithm is mysterious but very succinct. The generator presented here, SimpleRNG, uses Marsaglia's MWC (multiply with carry) algorithm. He's come up with some simple algorithms that nevertheless produce high quality output. George Marsaglia is one of the leading experts in random number generation. Also, the attributes of the generator could change without notice when you apply a service pack. The statistical quality of the built-in generator might not be adequate for some tasks.(The results still might not match due to other differences.) But if both programs use the same algorithm, such as the one used here, the results might be directly comparable. If both programs use their own library's random number generator, the outputs are not directly comparable. For example, at my work we often take prototype code that was written in R and rewrite it in C++ to make it more efficient. Sometimes it is necessary to compare the output of programs written in different languages.Also, it may be helpful to change the generator temporarily, making the output predictable to help debug code that uses the generator. You may want to examine the internal state of the generator, and it helps if that state is small.

#Random generator algorithm full

When debugging, it's convenient to have full access to the random number generator.

However, sometimes it helps to have your own random number generator. NET runtime would be the most convenient. NET Random Number Generator?įor many applications, it hardly matters what random number generator you use, and the one included in the. SimpleRNG can be used to generate random unsigned integers and double values with several statistical distributions: Because it is so simple, it is easy to drop into projects and easy to debug into. The generator uses a well-tested algorithm and is quite efficient.

This article will describe SimpleRNG, a very simple random number generator. And code using random number generators is tricky to test. Code implementing the algorithms is tricky to test. Good random number generation algorithms are tricky to invent. Random number generation is tricky business.