diff options
| -rwxr-xr-x | train.py | 70 |
1 files changed, 38 insertions, 32 deletions
@@ -26,7 +26,6 @@ from numpy import linalg as LA def normalise_faces(average_face, faces): faces = np.subtract(faces, np.tile(average_face, (faces.shape[0],1))) return np.divide(faces.T, np.std(faces.T, axis=0)).T - # Split data into training and testing sets def test_split(n_faces, raw_faces, split, seed): random.seed(seed) @@ -50,7 +49,6 @@ def test_split(n_faces, raw_faces, split, seed): faces_test = faces_test.reshape(n_faces*n_test_faces, n_pixels) return faces_train, faces_test, target_train, target_test - # usage: train.py [-h] -i DATA -o MODEL [-m M] parser = argparse.ArgumentParser() parser.add_argument("-i", "--data", help="Input CSV file", required=True) @@ -65,10 +63,11 @@ parser.add_argument("-t", "--split", help="Fractoin of data to use for testing", parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components", action='store_true') parser.add_argument("-p", "--pca", help="Use PCA", action='store_true') parser.add_argument("-l", "--lda", help="Use LDA", action='store_true') +parser.add_argument("-r", "--reconstruct", help="Use PCA reconstruction, specify face NR", type=int, default=0) +parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true') + args = parser.parse_args() -if args.pca and args.lda: - sys.exit("Flags -p and -l are mutually exclusive") M = args.eigen @@ -80,50 +79,57 @@ n_faces = 10 faces_train, faces_test, target_train, target_test = test_split(n_faces, raw_faces, args.split, args.seed) # This remove the mean and scales to unit variance -#sc = StandardScaler() +sc = StandardScaler() #faces_train = sc.fit_transform(faces_train) #faces_test = sc.transform(faces_test) explained_variances = () -if args.lda: - average_face = np.mean(faces_train, axis=0) - n_cases = 52 -# lda = LinearDiscriminantAnalysis(n_components=M) -# faces_train = lda.fit_transform(faces_train, target_train) -# faces_test = lda.transform(faces_test) -# explained_variances = lda.explained_variance_ratio_ -### FIND MEAN OF EACH CLASS - n_training_faces = int(round(n_cases*(1 - args.split))) - n_test_faces = n_cases - n_training_faces - mean_vector = np.zeros(10) - for n in range (10): - mean_acc = 0 - for x in range (int(np.divide(n_training_faces,10))): - mean_acc = np.add(mean_acc, np.mean(faces_train[x + n*10], axis=0)) - mean_vector [n] = np.divide(mean_acc, np.divide(n_training_faces,10)) - print (mean_vector) -### SCATTER MATRIX - for n in range (10) - faces_train = normalise_faces(mean_vector[n], faces_train[ -else: +if args.pca or (args.pca and args.lda) or args.pca_r: # faces_pca containcts the principial components or the M most variant eigenvectors average_face = np.mean(faces_train, axis=0) faces_train = normalise_faces(average_face, faces_train) faces_test = normalise_faces(average_face, faces_test) - e_vals, e_vecs = LA.eigh(np.cov(faces_train.T)) + 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 = 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_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 + -# Plot the variances (eigenvalues) from the pca object +if args.lda or (args.pca and args.lda): + lda = LinearDiscriminantAnalysis(n_components=M) + faces_train = lda.fit_transform(faces_train, target_train) + faces_test = lda.transform(faces_test) + class_means = lda.means_ + e_vals = lda.explained_variance_ratio_ + if args.faces: if args.lda: - sys.exit("Can not plot eigenfaces when using LDA") - for i in range(args.faces): - ax = plt.subplot(2, args.faces/2, i + 1) - ax.imshow(e_vecs[i].reshape([46, 56]).T, cmap = 'gist_gray') + for i in range (10): + ax = plt.subplot(2, 5, i + 1) + ax.imshow(class_means[i].reshape([46,56]).T) + else: + for i in range(args.faces): + ax = plt.subplot(2, args.faces/2, i + 1) + ax.imshow(e_vecs[i].reshape([46, 56]).T, cmap = 'gist_gray') plt.show() if args.principal: |