def test_shape(self): x = load_and_center_dataset(mnist_path) S = get_covariance(x) _, U = get_eig(S, 2) # This is the image of the "9" in the spec projected = project_image(x[3], U) self.assertEqual(np.shape(projected), (784,)) self.assertAlmostEqual(np.min(projected), -113.79455198736488) self.assertAlmostEqual(np.max(projected), 120.0658469887994)
import utilities as ut import pca import os path = '../data/' database = 'orl_faces' subspace = 'orl_subspace.npz' components = 400 rows = 112 columns = 92 if not os.path.exists(path + subspace): M = ut.load_images(database) eigenvalues, W, mu = pca.create_subspace(M, components) pca.save_subspace(path + subspace, eigenvalues, W, mu) else: eigenvalues, W, mu = pca.load_subspace(path + subspace) img = ut.create_image(rows,columns, 3) x = pca.project_image(img,W,mu) y = pca.reverse_projection(x,W,mu) image = ut.normalize_image(ut.unflatten_image(y,rows,columns)) original = ut.unflatten_image(img,rows,columns) ut.display_image(image) ut.display_image(original)
a1 = axs[0].imshow(orig.reshape((28, 28)), aspect='equal', cmap='gray')
axs[0].set_title("Original")
for i in range(len(projs)):
a = axs[i + 1].imshow(projs[i].reshape((28, 28)),
aspect='equal',
cmap='gray')
axs[i + 1].set_title(titles[i])
plt.show()
if __name__ == '__main__':
data = pca.load_and_center_dataset("mnist.npy")
cov = pca.get_covariance(data)
_, eig10 = pca.get_eig(cov, 10)
_, eig25 = pca.get_eig(cov, 25)
_, eig100 = pca.get_eig(cov, 100)
_, eig250 = pca.get_eig(cov, 250)
_, eig500 = pca.get_eig(cov, 500)
image = data[782]
projs = (pca.project_image(image,
eig500), pca.project_image(image, eig250),
pca.project_image(image, eig100), pca.project_image(image, eig25),
pca.project_image(image, eig10))
titles = ("n_components=500", "n_components=250", "n_components=100",
"n_components=25", "n_components=10")
display_multiple_images(image, projs, titles)
import pca
import os
path = '../data/'
database = 'orl_faces/'
subspace = 'orl_subspace.npz'
image_path = database + 's10/1.pgm'
components = 400
rows = 112
columns = 92
if not os.path.exists(path + subspace):
M = ut.load_images(database)
eigenvalues, W, mu = pca.create_subspace(M, components)
pca.save_subspace(path + subspace, eigenvalues, W, mu)
else:
eigenvalues, W, mu = pca.load_subspace(path + subspace)
img = ut.load_image(image_path)
num_components = [1,2,3,4,5,6,7,8,9,10,15,20,50,75,100,150,200,400]
for i in num_components:
x = pca.project_image(img,W[:,1:i],mu)
y = pca.reverse_projection(x,W[:,1:i],mu)
image = ut.normalize_image(ut.unflatten_image(y,rows,columns))
original = ut.unflatten_image(img,rows,columns)
ut.display_image(image, str(i) + ' components')
ut.display_image(original, 'original')
