Eigenvalues are scalar solutions to the equation
Ax = ax, where
A is a two-dimensional matrix and
x is a one-dimensional vector. Eigenvectors are vectors corresponding to eigenvalues.
Eigenvalues and eigenvectors are fundamental in mathematics and are used in many important algorithms, such as Principal Component Analysis (PCA). PCA can be used to simplify the analysis of large datasets.
eigvals() subroutine in the
numpy.linalg package computes eigenvalues. The
eig() function gives back a tuple holding eigenvalues and eigenvectors.
We will obtain the eigenvalues and eigenvectors of a matrix with the
eig() functions of the
numpy.linalg subpackage. We will check the outcome by ...