如何使用 NumPY 计算给定方阵的特征值和右特征向量？

在本文中，我们将讨论如何使用 NumPy 库计算给定方阵的特征值和右特征向量。

例子：

Suppose we have a matrix as: 
[[1,2],
[2,3]] 

Eigenvalue we get from this matrix or square array is: 
[-0.23606798 4.23606798]

Eigenvectors of this matrix are: 
[[-0.85065081 -0.52573111], 
[ 0.52573111 -0.85065081]] 

要了解它们的数学计算方式，请参见 EigenValues 和 EigenVectors 的计算。在下面的示例中，我们使用numpy.linalg.eig()来查找给定方形数组的特征值和特征向量。

Syntax: numpy.linalg.eig()

Parameter: An square array.

Return: It will return two values first is eigenvalues and second is eigenvectors.

示例 1：

Python3

# importing numpy library
import numpy as np
  
# create numpy 2d-array
m = np.array([[1, 2],
              [2, 3]])
  
print("Printing the Original square array:\n",
      m)
  
# finding eigenvalues and eigenvectors
w, v = np.linalg.eig(m)
  
# printing eigen values
print("Printing the Eigen values of the given square array:\n",
      w)
  
# printing eigen vectors
print("Printing Right eigenvectors of the given square array:\n"
      v)

输出：

Printing the Original square array:
 [[1 2]
 [2 3]]
Printing the Eigen values of the given square array:
 [-0.23606798  4.23606798]
Printing Right eigenvectors of the given square array:
 [[-0.85065081 -0.52573111]
 [ 0.52573111 -0.85065081]]

示例 2：

Python3

# importing numpy library
import numpy as np
  
# create numpy 2d-array
m = np.array([[1, 2, 3],
              [2, 3, 4],
              [4, 5, 6]])
  
print("Printing the Original square array:\n",
      m)
  
# finding eigenvalues and eigenvectors
w, v = np.linalg.eig(m)
  
# printing eigen values
print("Printing the Eigen values of the given square array:\n",
      w)
  
# printing eigen vectors
print("Printing Right eigenvectors of the given square array:\n",
      v)

输出：

Printing the Original square array:
 [[1 2 3]
 [2 3 4]
 [4 5 6]]
Printing the Eigen values of the given square array:
 [ 1.08309519e+01 -8.30951895e-01  1.01486082e-16]
Printing Right eigenvectors of the given square array:
 [[ 0.34416959  0.72770285  0.40824829]
 [ 0.49532111  0.27580256 -0.81649658]
 [ 0.79762415 -0.62799801  0.40824829]]