diff options
| -rwxr-xr-x | train.py | 32 |
1 files changed, 15 insertions, 17 deletions
@@ -89,32 +89,30 @@ explained_variances = () if args.pca or args.pca_r: # faces_pca containcts the principial components or the M most variant eigenvectors average_face = np.mean(faces_train, axis=0) + deviations = np.std(faces_train, axis=0) faces_train = normalise_faces(average_face, faces_train) faces_test = normalise_faces(average_face, faces_test) if (args.pca_r): e_vals, e_vecs = LA.eigh(np.cov(faces_train)) - e_vecs_original = e_vecs e_vecs = np.dot(faces_train.T, e_vecs) - # e_vecs = normalise_faces(np.mean(e_vecs,axis=0), e_vecs) - e_vecs = sc.fit_transform(e_vecs) - ###TODO Maybe replace with our normalising function - - if (args.reconstruct): - rec_vec = np.divide(average_face, np.std(average_face)).T - e_vecs_t = e_vecs.T - for i in range (M): - rec_vec = np.add(rec_vec, np.dot(e_vecs_original[i][args.reconstruct], e_vecs_t[i])) - plt.imshow(rec_vec.reshape([46,56]).T, cmap = 'gist_gray') - plt.show() else: e_vals, e_vecs = LA.eigh(np.cov(faces_train.T)) + # e_vecs = normalise_faces(np.mean(e_vecs,axis=0), e_vecs) + # e_vecs = sc.fit_transform(e_vecs) + + e_vals = np.flip(e_vals)[:M] + e_vecs = np.fliplr(e_vecs).T[:M] + deviations = np.flip(deviations) - e_vals = np.flip(e_vals) - e_vecs = np.fliplr(e_vecs).T - faces_train = np.dot(faces_train, e_vecs[:M].T) - faces_test = np.dot(faces_test, e_vecs[:M].T) -#FOR THE ASSESSMENT PRINT EIGENVALUES AND EIGENVECTORS OF BOTH CASES AND COMPARE RESULTS WITH PHYSICAL EXPLAINATIONS + faces_train = np.dot(faces_train, e_vecs.T) + faces_test = np.dot(faces_test, e_vecs.T) + if (args.reconstruct): + for face in range(args.reconstruct): + rec_vec = np.add(average_face, np.dot(faces_train[face], e_vecs) * deviations) + ar = plt.subplot(2, args.reconstruct/2, face + 1) + ar.imshow(rec_vec.reshape([46,56]).T, cmap = 'gist_gray') + plt.show() if args.lda or (args.pca and args.lda): lda = LinearDiscriminantAnalysis(n_components=M) |