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Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1). The dimensions of the returned array, must be non-negative.
class numpy.random.Generator(bit_generator) #. Container for the BitGenerators. Generator exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None.
There are 6 general mechanisms for creating arrays: You can use these methods to create ndarrays or Structured arrays. This document will cover general methods for ndarray creation. NumPy arrays can be defined using Python sequences such as lists and tuples. Lists and tuples are defined using [...] and (...), respectively.
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.
Return random floats in the half-open interval [0.0, 1.0). Results are from the “continuous uniform” distribution over the stated interval. To sample U n i f [a, b), b> a use uniform or multiply the output of random by (b - a) and add a: (b - a) * random() + a. Parameters: sizeint or tuple of ints, optional. Output shape.
random.normal(loc=0.0, scale=1.0, size=None) #. 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 Laplace independently [2], is often called the bell curve because of its characteristic shape (see the example ...
Simple random data ¶. rand (d0, d1, …, dn) Random values in a given shape. randn (d0, d1, …, dn) Return a sample (or samples) from the “standard normal” distribution. randint (low [, high, size, dtype]) Return random integers from low (inclusive) to high (exclusive).
numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None) #. Create an array. Parameters: objectarray_like. An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.
Alias for random_sample. Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1).
numpy.random.sample. #. random.sample(*args, **kwargs) #. This is an alias of random_sample. See random_sample for the complete documentation.