如何在 numpy repo 中找到方法实现

Question

我刚刚阅读了有关生成正常随机变量的 Box-Mueller 方法，并想看看 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，但还没有运气。 “随机”文件夹似乎没有我感兴趣的代码。特别是我想要调用时调用的代码 -

import numpy as np
rand = np.random.normal(size=10)

有没有人能指出我的这部分代码并大致解释如何有效地执行这些类型的搜索。

编辑：在这种情况下，下面的代码行没有太大帮助，因为它只是指向 init.py 文件，其中没有太多内容。

print(numpy.random.__file__)

Answer 1

您要查找的代码似乎在这里：

https://github.com/numpy/numpy/blob/464f79eb1d05bf938d16b49da1c39a4e02506fa3/numpy/random/mtrand/mtrand.pyx#L1551

如您所见，它位于random/mtrand/mtrand.pyx 下。如果您想知道 .pyx：Cython 声明：

"Cython 将 .pyx 文件编译为 .c 文件，其中包含 Python 扩展模块的代码。.c 文件由 C 编译器编译为 .so 文件（或 Windows 上的 .pyd）可以直接导入到 Python 会话中。”

您正在寻找normal 的定义，所以我搜索了"def normal"。

这是该链接的代码：

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)

正如duplicate question 中提到的（我在回答这个问题后发现的），您也可以尝试以下方法（但在这种情况下，它可能不会比您自己做的更进一步）：

import numpy.random
print(numpy.random.__file__)

# /home/adam/.pyenv/versions/datasci/lib/python3.6/site-packages/numpy/random/__init__.py

要跟踪到rk_gauss 的连接，您将在上面的代码中看到rk_normal 链接到：

double rk_normal(rk_state *state, double loc, double scale)
{
    return loc + scale*rk_gauss(state);
}

所以它：

Here 到 here 到 here。我认为这只是看看你感兴趣的函数调用了哪些其他函数。