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| normal(...) method of 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]_.
Parameters
----------
loc : float or array_like of floats
Mean ("centre") of the distribution.
scale : float or array_like of floats
Standard deviation (spread or "width") of the distribution.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. If size is ``None`` (default),
a single value is returned if ``loc`` and ``scale`` are both scalars.
Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
loc：均值（数学期望为μ） ，scale：标准差 （标准方差为σ2，标准差就是去掉平放），size：抽取样本的size
通过文档可以看到size参数可以是一个数字，或者是一个元组。他来决定了输出结果的形状。
如果传入的是一个单个数字 30，返回就会是30个长度的列表.
Returns
-------
out : ndarray or scalar
Drawn samples from the parameterized normal distribution.
返回是一个数组和这种类型数组和list很像。
See Also
--------
scipy.stats.norm : probability density function, distribution or
cumulative density function, etc.
Notes
-----
The probability density for the Gaussian distribution（高斯分布） is
.. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
这个是高斯分布是函数式。
where :math:`\mu` is the mean（平均数） and :math:`\sigma` the standard
deviation. The square of the standard deviation, :math:`\sigma^2`,
is called the variance.
The function has its peak at the mean, and its "spread" increases with
the standard deviation (the function reaches 0.607 times its maximum at
:math:`x + \sigma` and :math:`x - \sigma` [2]_). This implies that
`numpy.random.normal` is more likely to return samples lying close to
the mean, rather than those far away.
References
----------
.. [1] Wikipedia, "Normal distribution",
http://en.wikipedia.org/wiki/Normal_distribution
.. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
Random Variables and Random Signal Principles", 4th ed., 2001,
pp. 51, 51, 125.
Examples
--------
Draw samples from the distribution:
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
Verify the mean and the variance:
>>> abs(mu - np.mean(s)) < 0.01
True
>>> abs(sigma - np.std(s, ddof=1)) < 0.01
True
Display the histogram of the samples, along with
the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
... linewidth=2, color='r')
>>> plt.show()
n = np.random.normal(loc=0.0, scale=1, size=(3,3,3))
print(n)
上面输出下面的一个列表。可以看到size参数 如果是3个数字的元组，就返回3*3*3的数组，
[[[ 1.0172058 -0.44269518 0.26462677]
[-0.35355925 0.6063244 1.26014832]
[-0.18538448 -0.49259078 -0.62822534]]
[[ 0.95726339 -0.06239384 -1.56474133]
[ 1.42287373 -0.50173702 2.1642026 ]
[-0.54096807 0.2472884 -1.1990265 ]]
[[ 0.15663884 -0.18501496 -1.80360639]
[ 0.81581949 -2.73858599 0.34537614]
[-0.50873844 0.0351258 0.14204044]]
]
n = np.random.normal(loc=0.0, scale=1, size=(2,3))
print(n)
size参数 如果是2个数字的元组，就返回2*3的 数组，
[[-0.45735578 -0.53921269 -0.67449221]
[-0.98068719 -0.37125721 -0.43999013]]
import pprint
n = np.random.normal(loc=0.0, scale=1, size=30)
pprint.pprint(n)
array([ 0.59407443, -1.36867189, -0.32986369, 0.80101075, 0.72632235,
0.347779 , 0.10520544, 0.9492837 , 2.19274468, 1.59970721,
-0.95508177, -1.12671986, -0.53202767, 0.25783216, -1.1101487 ,
0.78002647, -0.14404636, -1.50865102, 1.29681861, -0.67255912,
-0.97184549, -0.30896753, 0.94493543, 0.686387 , 0.89299833,
-0.17019804, -0.12766749, -0.30600834, -0.0332422 , -0.05667029])
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