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21 lines
598 B
Python
21 lines
598 B
Python
# Select a principal eigenvector via NumPy
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# to be used as a template (copy & paste) script
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import numpy as np
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# set A to be your matrix
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A = np.array([[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]])
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eig_vals, eig_vecs = np.linalg.eig(A)
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idx = np.absolute(eig_vals).argsort()[::-1] # decreasing order
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sorted_eig_vals = eig_vals[idx]
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sorted_eig_vecs = eig_vecs[:, idx]
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principal_eig_vec = sorted_eig_vecs[:, 0] # eigvec with largest eigval
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normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec))
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print(normalized_pr_eig_vec) # eigvec that sums up to one
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