classified_label = classified_label.reshape(len(classified_label), 1)
return classified_label
def sum_rule(W, l_mean, test_image):
means_project = W.T @ l_mean
data_project = W.T @ test_image.T
norm_x = np.linalg.norm(data_project, axis=0).reshape(
1, len(np.linalg.norm(data_project, axis=0))) #1*n
norm_means = np.linalg.norm(means_project, axis=0).reshape(
1, len(np.linalg.norm(means_project, axis=0))) #1*52
p = (1 + (data_project.T @ means_project) / (norm_x.T @ norm_means)) / 2
return p
data = split_data()
train_im, train_l = data['train']
test_im, test_l = data['test']
train_im = train_im.T
test_im = test_im.T
train_l = train_l.T
test_l = test_l.T
height, width = (46, 56)
D = height * width
#architecture_flag = 1 # 0: Data Sampling 1: FeatureSpace Sampling
T = 60
# def rad_sum_classifier (T, train_im, train_l, test_im, test_l, D):
Num_sample, M0, M1 = 416, 150, 80
M_lda = 46
import numpy as np
import matplotlib.pyplot as plt
import time
SHAPE = (46, 56)
# split data preprocessor
from Split import split_data
data = split_data('face', 0.8, 13)
X_train, Y_train = data['train']
X_test, Y_test = data['test']
print(data)
# mean face
mean_face = X_train.mean(axis=1).reshape(-1, 1)
# plt.imshow(mean_face.reshape(SHAPE).T,
# cmap=plt.get_cmap('gray'), vmin=0, vmax=255)
# plt.title('Mean Face\n')
# plt.savefig('img/mean_face.png', dpi=1000, transparent=True)
D, N = X_train.shape
A = X_train - mean_face
t = time.time()
# high dimension
# S = (1 / N) * np.dot(A, A.T)
# low dimension
S = (1 / N) * np.dot(A.T, A)
# Calculate eigenvalues 'w' and eigenvectors 'v'
