Consider a randomly generated __m256i vector. Is there a faster precise way to convert them into __m256 vector of floats between 0 (inclusively) and 1 (exclusively) than division by float(1ull<<32)?

## What is the true maximum (and minimum) value of Random.nextGaussian()?

In theory, the bounds for nextGaussian are meant to be positive and negative infinity. But since Random.nextDouble, which is used to calculate the Gaussian random number, doesn't come infinitely close to 0 and 1, there is a practical limit to nextGaussian. And Random.next is also not a perfectly uniform distribution.

## If statement using IO Int haskell

I have a game , user vs computer and I want to randomly choose who starts the game. I have This gets a random number 0 or 1. However it is a IO Int, so I can't have an if statement comparing it to a number like

## How can I restart a CSS animation with random values in a loop?

I have an element which is randomly animated with CSS and JS with the help of CSS custom properties in the following way:

## std::mt19937 fails when std::uint_fast32_t is 4 bytes in GCC

The problem I have encountered occurs when I'm trying to test the cppreference example on generating pseudo-random numbers. Given the example:

## Random replace using Swift

I am experiencing a problem that I am not sure how to solve and I hope someone here can help me. Currently I have a string variable and later I replace the letters in the string with underscores like the following:

## Generating pseudo-random 16-bit integers

I need to generate 16-bit pseudo-random integers and I am wondering what the best choice is.The obvious way that comes in my mind is something as follows:

## Ways of getting 20% of elements in array – PHP

I have an array of n elements and I need to get random 20% of those elements into another array. Is there any function which can achieve this?

## How to generate random numbers with each random number having a difference of at least x with all other elements?

I know this goes against the definition of random numbers, but still I require this for my project. For instance, I want to generate an array with 5 random elements in range(0, 200).

## C++ random yields different numbers for same Mersenne Twister seed when using float precision

I need to run reproducible Monte Carlo runs. That means I use a known seed that I store with my results, and use that seed if I need to run the same problem instance using the same random numbers. This is common practice.