http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html
#np.random.normal,产生制定分布的数集
#http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html
# mean and standard deviation
# 均值的物理意义mu,Mean (“centre”) of the distribution.
# 方差的物理意义sigma,Standard deviation (spread or “width”) of the distribution
import numpy as np
mu, sigma = 0, 0.1
s = np.random.normal(mu, sigma, 1000)
#验证均值和方差,是否和随机生成的一样
print(abs(mu - np.mean(s)) < 0.01)
print(abs(sigma - np.std(s, ddof=1)) < 0.01) #ddof不知道什么意思
import matplotlib.pyplot as plt
count, bins, ignored = plt.hist(s, 10, normed=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()
numpy.random.normal
- 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 [R250], 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 [R250].
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.
