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numpy. random. rand — NumPy v2. 3 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 array, must be non-negative If no argument is given a single Python float is returned Returns: out ndarray, shape (d0, d1, , dn) Random values
numpy. random. uniform — NumPy v2. 3 Manual numpy random uniform# random uniform (low = 0 0, high = 1 0, size = None) # 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 interval is equally likely to be drawn by uniform
numpy. random. randint — NumPy v2. 3 Manual >>> np random randint (5, size = (2, 4)) array([[4, 0, 2, 1], # random [3, 2, 2, 0]]) Generate a 1 x 3 array with 3 different upper bounds >>> np random randint ( 1 , [ 3 , 5 , 10 ]) array([2, 2, 9]) # random
Random sampling — NumPy v2. 3 Manual The numpy random module implements pseudo-random number generators (PRNGs or RNGs, for short) with the ability to draw samples from a variety of probability distributions In general, users will create a Generator instance with default_rng and call the various methods on it to obtain samples from different distributions
Random Generator — NumPy v2. 3 Manual >>> import numpy as np >>> rng = np random default_rng (12345) >>> print (rng) Generator(PCG64) >>> rfloat = rng random >>> rfloat 0 22733602246716966 >>> type (rfloat) <class 'float'> Here we use default_rng to generate 3 random integers between 0 (inclusive) and 10 (exclusive):
numpy. random. normal — NumPy v2. 3 Manual numpy random normal# random normal (loc = 0 0, scale = 1 0, size = None) # Draw random samples from a normal (Gaussian) distribution
numpy. random. rand — NumPy v1. 15 Manual numpy random rand (d0, d1, , dn) ¶ Random values in a given shape Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)
Random sampling (numpy. random) — NumPy v2. 1 Manual Random sampling (numpy random)# Quick start # The numpy random module implements pseudo-random number generators (PRNGs or RNGs, for short) with the ability to draw samples from a variety of probability distributions
numpy. random. random — NumPy v1. 16 Manual numpy random random (size=None) ¶ Return random floats in the half-open interval [0 0, 1 0) Results are from the “continuous uniform” distribution over the stated interval
numpy. random. choice — NumPy v2. 3 Manual >>> np random choice (5, 3, replace = False, p = [0 1, 0, 0 3, 0 6, 0]) array([2, 3, 0]) # random Any of the above can be repeated with an arbitrary array-like instead of just integers For instance: