NumPy Pseudorandom Number Generation: Difference between revisions
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array([-0.31574999, -0.22620981, 0.78084923, -0.08299547, 0.02537403, | array([-0.31574999, -0.22620981, 0.78084923, -0.08299547, 0.02537403, -0.39786547, 0.69732466, -1.76220064, 0.26101635, -0.32073839]) | ||
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[-1.25755550e+00, 2.60187554e-01, 5.62396877e-01]]) | [-1.25755550e+00, 2.60187554e-01, 5.62396877e-01]]) | ||
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<font color=darkkhaki>TODO: https://learning.oreilly.com/library/view/python-for-data/9781098104023/ch04.html#:-:text=These%20random%20numbers%20are%20not%20truly%20random</font>. | |||
Latest revision as of 17:39, 21 May 2024
Internal
Overview
The numpy.random module supplements the built-in Python random module with functions for efficiently generating whole arrays of sample values from many kinds of probability distributions.
np.random.standard_normal(10)
array([-0.31574999, -0.22620981, 0.78084923, -0.08299547, 0.02537403, -0.39786547, 0.69732466, -1.76220064, 0.26101635, -0.32073839])
np.random.standard_normal(size=(5, 3))
array([[ 6.26367775e-01, -7.97741988e-01, 1.53270456e-01],
[ 1.66227193e+00, -1.16015757e-03, 9.65301493e-01],
[-8.90605112e-01, 8.53983237e-01, -4.24828642e-01],
[-4.60834281e-01, -1.36535277e+00, 1.89016088e+00],
[-1.25755550e+00, 2.60187554e-01, 5.62396877e-01]])