January 2018
Beginner to intermediate
316 pages
7h 14m
English
Just as we did in PCA, we rely on eigenvalue decompositions of a specific matrix. In the case of LDA, we will be decomposing the matrix
:
# calculate eigenvalues and eigenvectors of S−1W x SB eig_vals, eig_vecs = np.linalg.eig(np.dot(np.linalg.inv(S_W), S_B)) eig_vecs = eig_vecs.real eig_vals = eig_vals.real for i in range(len(eig_vals)): eigvec_sc = eig_vecs[:,i] print 'Eigenvector {}: {}'.format(i+1, eigvec_sc) print 'Eigenvalue {:}: {}'.format(i+1, eig_vals[i]) print Eigenvector 1: [-0.2049 -0.3871 0.5465 0.7138] Eigenvalue 1: 32.2719577997 Eigenvector 2: [ 0.009 0.589 -0.2543 0.767 ...Read now
Unlock full access