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)?
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.
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
I have an element which is randomly animated with CSS and JS with the help of CSS custom properties in the following way:
The problem I have encountered occurs when I'm trying to test the cppreference example on generating pseudo-random numbers. Given the example:
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:
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:
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?
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).
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.