diff options
| -rwxr-xr-x | train.py | 39 |
1 files changed, 25 insertions, 14 deletions
@@ -6,7 +6,6 @@ import matplotlib.pyplot as plt import sys import random -from numpy import linalg as LA from random import randint from sklearn.neighbors import KNeighborsClassifier @@ -21,11 +20,11 @@ import argparse import numpy as np from numpy import genfromtxt -# from numpy import linalg as LA +from numpy import linalg as LA # subtract the normal face from each row of the face matrix -def normalise_faces(average_face, raw_faces): - return np.subtract(raw_faces, np.tile(average_face, (raw_faces.shape[1],1)).T) +def normalise_faces(average_face, faces): + return np.subtract(faces, np.tile(average_face, (faces.shape[0],1))) # usage: train.py [-h] -i DATA -o MODEL [-m M] parser = argparse.ArgumentParser() @@ -60,8 +59,6 @@ def test_split(n_faces, raw_faces, split, seed): random.seed(seed) n_cases = 52 n_pixels = 2576 - - print(raw_faces.shape) raw_faces_split = np.split(raw_faces,n_cases) n_training_faces = int(round(n_cases*(1 - split))) @@ -83,9 +80,9 @@ def test_split(n_faces, raw_faces, split, seed): 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() -faces_train = sc.fit_transform(faces_train) -faces_test = sc.transform(faces_test) +#sc = StandardScaler() +#faces_train = sc.fit_transform(faces_train) +#faces_test = sc.transform(faces_test) explained_variances = () if args.lda: @@ -95,17 +92,31 @@ if args.lda: explained_variances = lda.explained_variance_ratio_ else: # faces_pca containcts the principial components or the M most variant eigenvectors - pca = PCA(svd_solver='full', n_components=M) - faces_train = pca.fit_transform(faces_train) - faces_test = pca.transform(faces_test) - explained_variances = pca.explained_variance_ratio_ +### FROM SKLEARN +# pca = PCA(svd_solver='full', n_components=M) +# faces_train = pca.fit_transform(faces_train) +# faces_test = pca.transform(faces_test) +# explained_variances = pca.explained_variance_ratio_ + +### FROM OLD CODE + average_face = np.mean(faces_train, axis=0) + plt.imshow(average_face.reshape(46,56)) + plt.show() + faces_train = normalise_faces(average_face, faces_train) + faces_test = normalise_faces(average_face, faces_test) + e_vals, e_vecs = LA.eigh(np.dot(faces_train.T, faces_train)) + print(e_vecs.shape) + explained_variances = e_vals[:M] + e_vecs =np.divide(e_vecs, LA.norm(e_vecs)) + faces_train = np.dot(faces_train, e_vecs[:M]) + faces_test = np.dot(faces_test, e_vecs[:M]) # Plot the variances (eigenvalues) from the pca object 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(pca.components_[i].reshape([46, 56]).T) + ax.imshow(e_vecs[i].reshape([46, 56]), cmap = 'gist_gray') plt.show() if args.principal: |