import numpy as np
mean = [0, 0]
cov = [[1, 0], [0, 5]]
import matplotlib.pyplot as plt
x, y = np.random.multivariate_normal(mean, cov, 100).T
plt.plot(x, y, 'x')
plt.axis('equal')
plt.show()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), m*n*k 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.
Returns:
out : ndarray
The drawn samples, of shape size, if that was provided. If not, the shape is (N,).
In other words, each entry out[i,j,…,:] is an N-dimensional value drawn from the distribution.
