Numpy基础 random.normal 从参数化的正态分布中随机抽取样本

•        Python : 3.8.11
•      numpy : 1.20.1
•          OS : Ubuntu Kylin 20.04
•       Conda : 4.10.1
•    jupyter lab : 3.1.4

 

代码示例

import numpy as np
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# normal 正态分布
# loc 均值、中心点
# scale 标准差
# size shape
np.random.normal(loc=0.0,scale=0.5,size=(2,3))

array([[-0.15069357,  0.26875581,  0.31638909],
       [-0.71754672, -0.05140111, -0.22627883]])
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np.random.normal(loc=0.0,scale=0.5,size=(2,3))

array([[-0.25230081, -0.46464863, -1.16539577],
       [-0.48035575,  0.32226706,  0.4507823 ]])
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源码学习

help(np.random.normal)

Help on built-in function normal:

normal(...) method of numpy.random.mtrand.RandomState instance
    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 below).
    
    The normal distributions occurs often in nature.  For example, it
    describes the commonly occurring distribution of samples influenced
    by a large number of tiny, random disturbances, each with its own
    unique distribution [2]_.
    
    .. note::
        New code should use the ``normal`` method of a ``default_rng()``
        instance instead; please see the :ref:`random-quick-start`.

......        
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