Question

我正在使用Scipy.stats.multivariate_normal从多元正态分布中提取样本。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()

每次运行时样本都不同。如何获得相同的样品？我期待的是：

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

或

samples = mn.rsv(seed = aNumber)

Answer 1

有两种方法：

rvs()方法接受random_state参数。它的价值可以是整数种子，或numpy.random.RandomState的实例。在这个例子，我使用整数种子：

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]])

您可以为numpy的全局随机数生成器设置种子。这是multivariate_normal.rvs()在未给出random_state时使用的生成器：

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]])

Scipy Multivariate Normal：如何绘制确定性样本？

1 个答案: