def test_invariance_to_many_transforms(self):
X = self.data(500)
pca = PCA(X)
x = pca.transform(X, ndims=2) # error introduced here
self.assertTrue(
np.allclose(np.cov(x.T), np.eye(2), atol=1e-4, rtol=1e-2))
X1 = pca.inverse_transform(x)
x1 = pca.transform(X1, ndims=2)
X2 = pca.inverse_transform(x1)
self.assertTrue(np.allclose(X1, X2))
def test_transform_inverse_transform(self):
X = self.data(500)
pca = PCA(X)
x = pca.transform(X, ndims=3)
self.assertTrue(
np.allclose(np.cov(x.T), np.eye(3), atol=1e-4, rtol=1e-2))
X_ = pca.inverse_transform(x)
self.assertTrue(np.allclose(X, X_))
class EigenFaceRecognizer:
pca = None
labels = None
trained_imgs = None
i_to_label = defaultdict(int)
def __init__(self):
self.pca = PCA()
self.labels = []
def train(self, mat, label):
tmp = []
for i in range(len(label)):
self.i_to_label[i] = label[i]
if i != 0 and label[i] != label[i - 1]:
self.labels.append(tmp)
tmp = []
tmp.append((i, label[i]))
self.labels.append(tmp)
to_fit = []
for i in range(len(mat)):
to_fit.append(np.ndarray.flatten(mat[i]))
self.trained_imgs = self.pca.fit_transform(to_fit)
def predict(self, img):
input_img = img
tmp_img = np.ndarray.flatten(img)
tmp_img = np.array([tmp_img])
tmp_img = self.pca.transform(tmp_img)
min_mean = 1e100
min_lable = 0
re_imgs = self.pca.inverse_transform(self.trained_imgs)
re_imgs = re_imgs.astype(np.uint8)
sum = 0
size = 0
for i in range(len(self.trained_imgs)):
if i != 0 and self.i_to_label[i] != self.i_to_label[i - 1]:
mean = sum / size
if mean < min_mean:
min_mean = mean
min_lable = self.i_to_label[i - 1]
sum = 0
size = 0
trained_img = self.trained_imgs[i]
distance = self.dis(tmp_img, trained_img)
size += 1
sum += distance
#print(self.i_to_label[i], distance)
mean = sum / size
if mean < min_mean:
min_mean = mean
min_lable = self.i_to_label[len(self.trained_imgs) - 1]
for i in self.i_to_label:
if self.i_to_label[i] == min_lable:
result_img = np.reshape(re_imgs[i], (100, 100))
tmp = np.concatenate((input_img, result_img))
break
sum = 0
size = 0
return [min_lable, min_mean]
def dis(self, a, b):
return np.linalg.norm(a - b)
