Numpy之高斯分布 multivariate_normal

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以下是官方说明文档链接

numpy.random.multivariate_normal

• 1.函数定义
  numpy.random.multivariate_normal(mean, cov[, size, check_valid, tol])
• 2.参数解释
  Parameters:
  mean : 1-D array_like, of length N
  Mean of the N-dimensional distribution.
  cov : 2-D array_like, of shape (N, N)
  Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling.
  size : int or tuple of ints, optional
  Given a shape of, for example, (m,n,k), mnk samples are generated, and packed in an m-by-n-by-k arrangement. Because each sample is N-dimensional, the output shape is (m,n,k,N). If no shape is specified, a single (N-D) sample is returned.
  check_valid : { ‘warn’, ‘raise’, ‘ignore’ }, optional
  Behavior when the covariance matrix is not positive semidefinite.
  tol : float, optional
  Tolerance when checking the singular values in covariance matrix.

• 3.使用
  （1）
  ‘‘‘
  import numpy as np
  import matplotlib.pyplot as plt
  Y = np.random.multivariate_normal([(0)], [[1]], 1000)
  plt.plot(Y,‘ro‘)
  ‘‘‘

采集1000个符合0均值，1协方差的高斯分布的点，下图纵轴表示采集到的点的值，横轴表示点的序号，可以看出所有点的值大致是符合N（0,1）分布的。

Numpy之高斯分布 multivariate_normal

标签：normal   检查   lib   ike   for   dom   Dimension   body   OLE   

原文地址：https://www.cnblogs.com/fish-lake/p/11579023.html