把一个矩阵行优先展成一个向量,numpy.ravel() vs numpy.flatten()区别

x = np.array([[1, 2], [3, 4]])
print（x.flatten()[1] = 100)
array([[1, 2],
       [3, 4]])            # flatten：返回的是拷贝，不会对数据进行修改


print(x.ravel()[1] = 100)  #ravel()则回对数据进行就该
array([[  1, 100],
       [  3,   4]])

x = np.array([[1, 2], [3, 4]])
array([[1, 2],
       [3, 4]])
x.flatten()
array([1, 2, 3, 4])
x.ravel()
array([1, 2, 3, 4])

x.flatten('F')
array([1, 3, 2, 4])
x.ravel('F')
array([1, 3, 2, 4])

x.reshape(-1)
array([1, 2, 3, 4])
x.T.reshape(-1)
array([1, 3, 2, 4])