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Bookmark or Share- Generate random numbers with a given (numerical) distributionI have a file with some probabilities for different values e.g.: 1 0.1 2 0.05 3 0.05 4 0.2 5 0.4 6 0.2 I would like to generate random numbers using this distribution. Does an existing module that
- python - scipy.stats seed? - Stack OverflowThe advantage of this method over assigning it to the random_state of rv_continuous or rv_discrete is, that you always have explicit control over the random state of your rvs, whereas with my_dist.random_state = np.random.RandomState(seed=342423) the seed is lost after each call to rvs, possibly resulting in non-reproducible results when losing ...
- Why `torch.cuda.is_available()` returns False even after installing ...2. How to check if your GPU/graphics driver supports a particular CUDA version. The graphics driver is the software that allows your operating system to communicate with your graphics card.
- What are the arguments for scipy.stats.uniform? - Stack Overflowuniform.rvs(loc=5, scale=45) Even though it's possible to call the distribution directly with parameters, ...
- python - Difference between random draws from scipy.stats....rvs and ...I saw what joon mentioned where, in particular, random numbers from the normal distribution were much more quickly generated with numpy than from rvs in scipy.stats. As user333700 mentioned there is some overhead with rvs but if you are generating an array of random values then that gap closes compared to numpy. Here is a jupyter timing example:
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