np.meshgrid参考
官方文档给出的解释

Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.

参数
indexing : {‘xy’, ‘ij’}, optional Cartesian (‘xy’, default) or matrix (‘ij’) indexing of output.
返回

 X1, X2,..., XN : ndarray

For vectors x1, x2,…, xn with lengths Ni=len(xi) , return (N1, N2, N3,...Nn) shaped arrays if indexing=‘ij’ or (N2, N1, N3,...Nn) shaped arrays if indexing=‘xy’ with the elements of xi repeated to fill the matrix along the first dimension for x1, the second for x2 and so on.

• 关于indexing,默认是xy形式的，即笛卡尔坐标系的形式。
• 如果输入向量 x1, x2,…, xn 的长度Ni=len(xi)，那么在indexing = 'xy'时返回(N2, N1, N3,...Nn)形式的numpy数组，indexing = ‘ij’时返回 (N2, N1, N3,...Nn)形式的numpy数组
• 如果xi是np.array形式的数据，在进行np.meshgrid会自动地将xi先展平，再进行操作
  下面x是维度为(2,3,4)地numpy数组，展平后是(24,)

x = np.linspace(1,15,24).reshape(2,3,4)
y = np.linspace(11,15,6)
z = np.linspace(21,25,5)xx,yy,zz = np.meshgrid(x,y,z)print(xx.shape) # (6,24,5)

• 对于numpy里面地矩阵(np.matrix)，则不会进行展平。而且，需要注意变量的维度

m, n = (5, 3)
x = np.linspace(0, 1, m)
y = np.linspace(0, 1, n)

x:array([ 0. , 0.25, 0.5 , 0.75, 1. ])
y:array([ 0. , 0.5, 1. ])

xx,yy = np.meshgrid(x,y) 

x 是m 维向量，y是n维向量，np.meshgrid(x,y)后的结果是(n,m)
得到的xx,是x在纵轴方向上复制n次，yy是y在横轴方向上复制m次

生成测试数据网格

grid_test = np.stack((xx.flat,yy.flat),axis=1)
grid_predict= gmm.predict(grid_test)

得到grid_test

[[0.   0.  ][0.25 0.  ][0.5  0.  ][0.75 0.  ][1.   0.  ][0.   0.5 ]
...]