How does rand() work in C? - Stack Overflow Like rand(), rand_r() returns a pseudo-random integer in the range [0, RAND_MAX] The seedp argument is a pointer to an unsigned int that is used to store state between calls If rand_r() is called with the same initial value for the integer pointed to by seedp, and that value is not modified between calls, then the same pseudo-random sequence
How do I get a specific range of numbers from rand ()? Related: How to generate a random int in C? Here is my answer there, which contains the definition for my int utils_rand(int min, int max) func, which returns a random number using rand() which is in the specific range from min to max, inclusive, thereby also answering this question
How does rand() work? Does it have certain tendencies? Is there . . . Now, if you are interested on the reasons why the above is true, here are the gory details on how rand() works: rand() is what's called a "linear congruential generator " This means that it employs an equation of the form: x n+1 = (*a****x n + ***b*) mod m where x n is the n th random number, and a and b are some predetermined integers
How to generate a random int in C? - Stack Overflow If we use more than 53 bits, we get rounding bias Some programmers write code like rand() (double)RAND_MAX, but rand() might return only 31 bits, or only 15 bits in Windows OpenSSL's RAND_bytes() seeds itself, perhaps by reading dev urandom in Linux
Whats the Right Way to use the rand () Function in C++? int randint() { int random = rand(); return random; } int main() { To get a unique sequence the random number generator should only be seeded once during the life of the application As long as you don't try and start the application mulitple times a second you can use time() to get a ever changing seed point that only repeats every
Differences between numpy. random. rand vs numpy. random. randn in Python . . . np random rand is for Uniform distribution (in the half-open interval [0 0, 1 0)) np random randn is for Standard Normal (aka Gaussian) distribution (mean 0 and variance 1) You can visually explore the differences between these two very easily:
c - Rand Implementation - Stack Overflow rand and srand are usually implemented as a simple LCG, you can easily write your own (it's few lines of code) without looking for the sources of rand and srand Notice that, if you need random numbers for "serious" purposes (e g cryptography), there are much better RNGs than LCG