In NumPy we can compute the eigenvalues and right eigenvectors of a given square array with the help of numpy.linalg.eig(). It will take a square array as a parameter and it will return two values first one is eigenvalues of the array and second is the right eigenvectors of a given square array.
Syntax: numpy.linalg.eig()
Parameter: An square array.
Return: It will return two values first is eigenvalues and second is eigenvectors.
Example 1:
Python
import numpy as np mat = np.mat("1 -2;1 3") # Original matrixprint(mat)print("")evalue, evect = np.linalg.eig(mat) # Eigenvalues of the said matrix"print(evalue)print("") # Eigenvectors of the said matrixprint(evect) |
Output:
[[ 1 -2] [ 1 3]] [2.+1.j 2.-1.j] [[ 0.81649658+0.j 0.81649658-0.j ] [-0.40824829-0.40824829j -0.40824829+0.40824829j]]
Example 2:
Python
import numpy as np mat = np.mat("1 2 3;1 3 4;3 2 1") # Original matrixprint(mat)print("")evalue, evect = np.linalg.eig(mat) # Eigenvalues of the said matrix"print(evalue)print("") # Eigenvectors of the said matrixprint(evect) |
Output:
[[1 2 3] [1 3 4] [3 2 1]] [ 6.70156212 0.29843788 -2. ] [[-0.5113361 -0.42932334 -0.40482045] [-0.69070311 0.7945835 -0.52048344] [-0.5113361 -0.42932334 0.75180941]]
