'''
We're writing Python using NumPy now. Let special value of a matrix mean the its determinant plus its largest eigenvalue. Write a function in Python that takes a list of matrices as input and returns the position of the matrix with the smallest special values
'''

import numpy as np

def find_matrix_with_smallest_special_value(matrices):
    """
    Finds the matrix with the smallest special value in a list of matrices.
    The special value of a matrix is defined as the determinant plus the largest eigenvalue.
    """
    smallest_special_value = float('inf')
    smallest_special_value_matrix_index = None
    for i, matrix in enumerate(matrices):
        det = np.linalg.det(matrix)
        eigenvalues = np.linalg.eigvals(matrix)
        largest_eigenvalue = np.max(eigenvalues)
        special_value = det + largest_eigenvalue
        if special_value < smallest_special_value:
            smallest_special_value = special_value
            smallest_special_value_matrix_index = i
    return smallest_special_value_matrix_index