I would like a function that can generate a pseudo-random sequence of values, but for that sequence to be repeatable every run. The data I want has to be reasonably well randomly distributed over a given range, it doesn’t have to be perfect.
I want to know the use of random.sample() method and what does it give? When should it be used and some example usage.
How do you create a random string in Python?
What is the efficient(probably vectorized with Matlab terminology) way to generate random number of zeros and ones with a specific proportion? Specially with Numpy?
I want to be able to pick values from a normal distribution that only ever fall between 0 and 1. In some cases I want to be able to basically just return a completely random distribution, and in other cases I want to return values that fall in the shape of a gaussian.
The random module (http://docs.python.org/2/library/random.html) has several fixed functions to randomly sample from. For example random.gauss will sample random point from a normal distribution with a given mean and sigma values.
I’m making a project in python and I would like to create a random number that is cryptographically secure, How can I do that? I have read online that the numbers generated by the regular randomizer are not cryptographically secure, and that the function os.urandom(n) returns me a string, and not a number.
I currently have a Python set of n size where n >= 0. Is there a quick 1 or 2 lines Python solution to do it? For example, the set will look like: fruits = set(['apple', 'orange', 'watermelon', 'grape']) The goal is to pick 2 random items from the above and it’s possible that the … Read more
If I do watch cat /proc/sys/kernel/random/entropy_avail I see that my systems entropy slowly increases over time, until it reaches the 180-190 range at which point it drops down to around 120-130. The drops in entropy seem to occur about every twenty seconds. I observe this even when lsof says that no process has /dev/random or /dev/urandom open. What is draining away the entropy? Does the kernel need entropy as well, or maybe it is reprocessing the larger pool into a smaller, better quality pool?
I have two sentence-aligned parallel corpora (text files) with about 50 mil words. (from the Europarl corpus -> parallel translation of legal documents).
I’d now like to shuffle the lines of the two files, but both in the same way. I wanted to approach that using gshuf (I’m on a Mac) using one unique random source.