python - Scipy 多元正态 : How to draw deterministic samples?

我正在使用 Scipy.stats.multivariate_normal 从多元正态分布中抽取样本。像这样:

from scipy.stats import multivariate_normal
# Assume we have means and covs
mn = multivariate_normal(mean = means, cov = covs)
# Generate some samples
samples = mn.rvs()

每次运行的样本都不同。我如何获得始终相同的样本？
我期待这样的事情:

mn = multivariate_normal(mean = means, cov = covs, seed = aNumber)

或者

samples = mn.rsv(seed = aNumber)

最佳答案

有两种方式:

rvs() 方法接受 random_state 参数。它的值(value)可以
是一个整数种子，或 numpy.random.RandomState 的一个实例。在
在这个例子中，我使用了一个整数种子:

In [46]: mn = multivariate_normal(mean=[0,0,0], cov=[1, 5, 25])

In [47]: mn.rvs(size=5, random_state=12345)
Out[47]:
array([[-0.51943872,  1.07094986, -1.0235383 ],
       [ 1.39340583,  4.39561899, -2.77865152],
       [ 0.76902257,  0.63000355,  0.46453938],
       [-1.29622111,  2.25214387,  6.23217368],
       [ 1.35291684,  0.51186476,  1.37495817]])

In [48]: mn.rvs(size=5, random_state=12345)
Out[48]:
array([[-0.51943872,  1.07094986, -1.0235383 ],
       [ 1.39340583,  4.39561899, -2.77865152],
       [ 0.76902257,  0.63000355,  0.46453938],
       [-1.29622111,  2.25214387,  6.23217368],
       [ 1.35291684,  0.51186476,  1.37495817]])

可以为 numpy 的全局随机数生成器设置种子。如果 multivariate_normal.rvs() 没有给出，这是 random_state 使用的生成器:

In [54]: mn = multivariate_normal(mean=[0,0,0], cov=[1, 5, 25])

In [55]: np.random.seed(123)

In [56]: mn.rvs(size=5)
Out[56]:
array([[  0.2829785 ,   2.23013222,  -5.42815302],
       [  1.65143654,  -1.2937895 ,  -7.53147357],
       [  1.26593626,  -0.95907779, -12.13339622],
       [ -0.09470897,  -1.51803558,  -4.33370201],
       [ -0.44398196,  -1.4286283 ,   7.45694813]])

In [57]: np.random.seed(123)

In [58]: mn.rvs(size=5)
Out[58]:
array([[  0.2829785 ,   2.23013222,  -5.42815302],
       [  1.65143654,  -1.2937895 ,  -7.53147357],
       [  1.26593626,  -0.95907779, -12.13339622],
       [ -0.09470897,  -1.51803558,  -4.33370201],
       [ -0.44398196,  -1.4286283 ,   7.45694813]])