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. 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.
0 kommentar(er)
