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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学习推荐


Python具有开源、跨平台、解释型、交互式等特性,值得学习。
Python的设计哲学:优雅,明确,简单。提倡用一种方法,最好是只有一种方法来做一件事。
代码的书写要遵守规范,这样有助于沟通和理解。
每种语言都有独特的思想,初学者需要转变思维、踏实践行、坚持积累。

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转载自juejin.im/post/7018030480377249828

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