If I compute the eigenvalues and eigenvectors using
numpy.linalg.eig (from Python), the eigenvalues returned seem to be all over the place. Using, for example, the Iris dataset, the normalized Eigenvalues are
[2.9108 0.9212 0.1474 0.0206], but the ones I currently have are
[9206.53059607 314.10307292 12.03601935 3.53031167].
The problem I'm facing is that I want to find out how much percentage of the variance each component brings, but with the current Eigenvalues I don't have the right values.
So, how can I transform my eigenvalues so that they can give me the correct proportion of variance?
Edit: Just in case it wasn't clear, I'm computing the
eig of the covariance matrix (The process is called Principal Component Analysis).