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  1. numpy.random.uniform — NumPy v2.2 Manual

    Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given …

  2. numpy.random.Generator.uniform — NumPy v2.2 Manual

    Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given …

  3. numpy.random.randint — NumPy v2.2 Manual

    Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).

  4. numpy.random.randint — NumPy v1.15 Manual

    Nov 4, 2018 · Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from …

  5. numpy.random.rand — NumPy v2.2 Manual

    Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1). Parameters: d0, d1, …, dn int, optional. The dimensions of the returned …

  6. numpy.random.normal — NumPy v2.2 Manual

    Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and …

  7. numpy.random.choice — NumPy v2.2 Manual

    Generate a uniform random sample from np.arange(5) of size 3 without replacement: >>> np . random . choice ( 5 , 3 , replace = False ) array([3,1,0]) # random >>> #This is equivalent to …

  8. numpy.random.vonmises — NumPy v2.2 Manual

    Draw samples from a von Mises distribution. Samples are drawn from a von Mises distribution with specified mode (mu) and concentration (kappa), on the interval [-pi, pi]. The von Mises …

  9. numpy.random.gamma — NumPy v2.2 Manual

    Draw samples from a Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale (sometimes designated …

  10. numpy.random.lognormal — NumPy v2.1 Manual

    Demonstrate that taking the products of random samples from a uniform distribution can be fit well by a log-normal probability density function. >>> # Generate a thousand samples: each is the …

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