Scipy 多元正态:如何绘制确定性样本

Question

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

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()

每次运行的样品都不同。如何始终获得相同的样品？我期待这样的东西：

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

或

samples = mn.rsv(seed = aNumber)

Answer 1

有两种方法：

1. rvs()方法接受random_state参数。 它的价值可以是整数种子，或者是 numpy.random.Generator 或 numpy.random.RandomState 的实例。 在这个例子，我使用一个整数种子：

    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]])
   

   此版本使用 numpy.random.Generator 的实例：

   In [34]: rng = np.random.default_rng(438753948759384)
   In [35]: mn = multivariate_normal(mean=[0,0,0], cov=[1, 5, 25])
   In [36]: mn.rvs(size=5, random_state=rng)
   Out[36]: 
   array([[ 0.30626179,  0.60742839,  2.86919105],
          [ 1.61859885,  2.63409111,  1.19018398],
          [ 0.35469027,  0.85685011,  6.76892829],
          [-0.88659459, -0.59922575, -5.43926698],
          [ 0.94777687, -5.80057427, -2.16887719]])
   

2. 您可以为 numpy 的全局随机数生成器设置种子。 如果未给出random_state，这是multivariate_normal.rvs()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]])