为什么 return np.random.normal(10 - 1. / (x + 0.1), 0.5) 有效
why return np.random.normal(10 - 1. / (x + 0.1), 0.5) works
正如我们在 numpy.random.normal
的文档中所见
numpy.random.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 [R217], 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 [R217]. Parameters:
loc : float
Mean (“centre”) of the distribution.
scale : float
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. Default is None, in which case a single value is
returned.
那为什么 np.random.normal(10 - 1. / (x + 0.1), 0.5) 在 x = 10**np.linspace(-2, 0, 8)
时有效
- 参数loc应该是float？
- 如果这有效，它的含义是什么？
您的代码绘制了 8 个数字，每个数字来自不同的高斯分布。 x 的值被视为分布参数，但每个值都用于生成该分布中的一个样本。
您的代码等同于：
np.random.normal(np.zeros(8), 0.5) + 10 - 1. / (x + 0.1)
即使用正态分布生成 8 个数字并将它们移动 x。
正如我们在 numpy.random.normal
的文档中所见numpy.random.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 [R217], 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 [R217]. Parameters:
loc : float
Mean (“centre”) of the distribution.
scale : float
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. Default is None, in which case a single value is returned.
那为什么 np.random.normal(10 - 1. / (x + 0.1), 0.5) 在 x = 10**np.linspace(-2, 0, 8)
- 参数loc应该是float？
- 如果这有效，它的含义是什么？
您的代码绘制了 8 个数字，每个数字来自不同的高斯分布。 x 的值被视为分布参数，但每个值都用于生成该分布中的一个样本。
您的代码等同于：
np.random.normal(np.zeros(8), 0.5) + 10 - 1. / (x + 0.1)
即使用正态分布生成 8 个数字并将它们移动 x。