def main():
if not os.path.isdir(training_set):
print("Cropping faces and making train and test sets")
face = FaceDetect()
face.face_return()
train_test_split.split()
print("Training....")
train_set = Eigen(training_set)
train_image_label = train_set.label_extract()
train_stacked_images, mean_face = train_set.image_processing()
_, eig_face = train_set.eigen_value()
# selecting no of eigen faces, the first values are the largest ones no need to sort them
eig_face = eig_face[:, :no_of_eigfaces]
train_weights, recons_train = train_set.weights_calculation(
eig_face, mean_face)
# display selected eigen faces
# train_set.display_data(eig_face, 'Selected Eigen faces')
#
# # display original images
# train_set.display_data(train_stacked_images, 'Original faces')
#
# # display reconstructed training face
# train_set.display_data(recons_train, 'reconstructed training data')
print("Training finished, testing ......")
test_set = Eigen(testing_set)
test_image_label = test_set.label_extract()
print('Original label:', test_image_label)
test_stacked_images, _ = test_set.image_processing(mean_face)
test_weights, recons_test = test_set.weights_calculation(
eig_face, mean_face)
# display original test face
test_set.display_data(test_stacked_images, 'original testfaces')
# display reconstructed test face
test_set.display_data(recons_test, 'reconstructed test data')
test = Test(train_stacked_images, train_weights, test_weights)
predicted_label = test.match_index(train_image_label)
print('Predicted label:', predicted_label)
match_check = [
1 if tl == pl else 0
for tl, pl in zip(test_image_label, predicted_label)
]
print(match_check)
print("Accuracy:", sum(match_check) / len(predicted_label))
# plt.scatter(x1[:, 0], x1[:, 1]) # plt.show() # 均值归一化 x2 = np.random.randint(0, 100, [50, 2]) x2 = np.array(x2, dtype=float) x2[:, 0] = (x2[:, 0] - np.mean(x2[:, 0])) / np.std(x2[:, 0]) x2[:, 1] = (x2[:, 1] - np.mean(x2[:, 1])) / np.std(x2[:, 1]) print(np.mean(x2), np.std(x2)) # plt.scatter(x2[:, 0], x2[:, 1]) # plt.show() iris = datasets.load_iris() X = iris.data Y = iris.target X_train, X_test, Y_train, Y_test = train_test_split.split(X, Y) # 使用sklearn的StandardScaler # from sklearn.preprocessing import StandardScaler # 使用自己写的StandarScaler from standard_scaler import StandardScaler std_scaler = StandardScaler() std_scaler.fit(X_train) print(std_scaler.mean_) print(std_scaler.scale_) X_train = std_scaler.transform(X_train) X_test = std_scaler.transform(X_test) # X_train_mean = []