FilesExpand file tree

 master

/

principal_eigenvector.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

20 lines (14 loc) · 598 Bytes

 master

/

principal_eigenvector.py

Copy path

Top

File metadata and controls

• Code
• Blame

20 lines (14 loc) · 598 Bytes

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

# Select a principal eigenvector via NumPy

# to be used as a template (copy & paste) script

import numpy as np

# set A to be your matrix

A = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

eig_vals, eig_vecs = np.linalg.eig(A)

idx = np.absolute(eig_vals).argsort()[::-1] # decreasing order

sorted_eig_vals = eig_vals[idx]

sorted_eig_vecs = eig_vecs[:, idx]

principal_eig_vec = sorted_eig_vecs[:, 0] # eigvec with largest eigval

normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec))

print(normalized_pr_eig_vec) # eigvec that sums up to one