我刚刚了解了用于生成普通随机变量的Box-Mueller方法，并想了解numpy python中的方法是否使用相同的方法。为此，我想看一下源代码。我一直在梳理GitHub repository，但还没有碰运气。文件夹“ random”似乎没有我感兴趣的代码。特别是，我想要在调用时调用的代码-
import numpy as np
rand = np.random.normal(size=10)
任何人都可以指出我这部分代码并大致解释如何有效地执行这类搜索。
编辑：在这种情况下，下面的代码行没有多大帮助，因为它仅指向其中没有太多内容的 init .py文件。
print(numpy.random.__file__)
答案 0 :(得分：2)
您正在寻找的代码似乎在这里：
如您所见，它位于random/mtrand/mtrand.pyx下。如果您想知道.pyx：Cython状态：
” Cython将一个.pyx文件编译成一个.c文件，其中包含Python扩展模块的代码。该c文件被C编译器编译成一个.so文件（在Windows上为.pyd）。可以直接导入到Python会话中。”
您在寻找normal的定义，所以我搜索了"def normal"。
这是该链接上的代码：
def normal(self, loc=0.0, scale=1.0, size=None):
"""
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.
Returns
-------
out : ndarray or scalar
Drawn samples from the parameterized normal distribution.
See Also
--------
scipy.stats.norm : probability density function, distribution or
cumulative density function, etc.
Notes
-----
The probability density for the Gaussian distribution is
.. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}
e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },
where :math:`\\mu` is the mean 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",
https://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()
"""
cdef ndarray oloc, oscale
cdef double floc, fscale
oloc = <ndarray>PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
if oloc.shape == oscale.shape == ():
floc = PyFloat_AsDouble(loc)
fscale = PyFloat_AsDouble(scale)
if np.signbit(fscale):
raise ValueError("scale < 0")
return cont2_array_sc(self.internal_state, rk_normal, size, floc,
fscale, self.lock)
if np.any(np.signbit(oscale)):
raise ValueError("scale < 0")
return cont2_array(self.internal_state, rk_normal, size, oloc, oscale,
self.lock)
如duplicate question（我在回答此问题后发现的）中所述，您也可以尝试以下方法（但在这种情况下，它可能无法比您自己做得更多）：
import numpy.random
print(numpy.random.__file__)
# /home/adam/.pyenv/versions/datasci/lib/python3.6/site-packages/numpy/random/__init__.py
要跟踪与rk_gauss的连接，您会在上面的代码中看到rk_normal，该链接链接到：
double rk_normal(rk_state *state, double loc, double scale)
{
return loc + scale*rk_gauss(state);
}
因此：