print("Loading Data")
X_train_face = load_training_data('face')
X_train_nonface = load_training_data('nonface')
[X_train_PCA_face, PCA_face_train] = perform_pca(X_train_face, size * size)
X_train_PCA_face = X_train_PCA_face.T
X_train_PCA_face = preprocess(X_train_PCA_face)
[X_train_PCA_nonface, PCA_nonface_train] = perform_pca(X_train_nonface,
size * size)
X_train_PCA_nonface = X_train_PCA_nonface.T
X_train_PCA_nonface = preprocess(X_train_PCA_nonface)
[mean_face, covar_face] = get_MC(X_train_PCA_face)
mix_face = Gaussian(mean_face.reshape(-1, 1), covar_face)
[mean_nonface, covar_nonface] = get_MC(X_train_PCA_nonface)
mix_nonface = Gaussian(mean_nonface.reshape(-1, 1), covar_nonface)
X_test_face = load_test_data('face')
X_test_nonface = load_test_data('nonface')
[X_test_face_PCA, PCA_face_test] = perform_pca(X_test_face, size * size)
X_test_face_PCA = X_test_face_PCA.T
X_test_face_PCA = preprocess(X_test_face_PCA)
[X_test_nonface_PCA, PCA_nonface_test] = perform_pca(X_test_nonface,
size * size)
X_test_nonface_PCA = X_test_nonface_PCA.T
expo2 = np.matmul(expo1, term3)[0, 0]
val = np.exp(expo2)
val = val / np.sqrt(np.linalg.det(self.sigma))
val = val / ((2 * np.pi)**(100 / 2))
return val
print("Loading Data")
X_train_face = load_train_data('face')
X_train_nonface = load_train_data('nonface')
X_CV_nonface = load_CV_data('nonface')
X_CV_face = load_CV_data('face')
print("Training")
mean_face, covar_face = get_MC(X_train_face)
mix_face = Gaussian(mean_face.reshape(-1, 1), covar_face)
mean_nonface, covar_nonface = get_MC(X_train_nonface)
mix_nonface = Gaussian(mean_nonface.reshape(-1, 1), covar_nonface)
print("Testing")
prob_face_facedata = array([])
prob_nonface_facedata = array([])
prob_face_nonfacedata = array([])
prob_nonface_nonfacedata = array([])
for i in range(len_CV):
inp_facedata = X_CV_face[i].reshape(-1, 1)
inp_nonfacedata = X_CV_nonface[i].reshape(-1, 1)