'''Illustrates Perron's theorem by computing eigenvalues and eigenvectors
of a random positive matrix.'''

import numpy as np

matrix = np.random.randint(1, 10, size=(6,6))

eigenvalues, eigenvectors = np.linalg.eig(matrix)

# Print the matrix and its eigenvalues and eigenvectors
print(" Matrix:\n", np.array2string(matrix, precision=2))

print("\n\n Eigenvalues:")
for val in eigenvalues:
    print(f"{val:.2f}")

print("\n\n Eigenvectors:")
for i, vec in enumerate(eigenvectors.T):  # Transpose for easier readability
    print(f"Vector {i+1}: {np.array2string(vec, precision=2)}")