def Bayes_id(root_dir):
global video_type_num, feature_dim
global emotion_type_num, valabel_type_num
splits_num = 5
#先获取全部数据
x = []
y = []
for i in range(splits_num):
#从文件获取数据
subjects, videos = get_subject(root_dir + 'subject/subject_video_' +
str(i) + '.txt')
features = np.loadtxt(root_dir + 'feature/EEG_feature_' + str(i) +
'.txt')
va_labels = np.loadtxt(root_dir + 'valabel/valence_arousal_label_' +
str(i) + '.txt',
dtype=int)
if 'HCI' in root_dir:
emotions = np.loadtxt(root_dir + 'emotion/EEG_emotion_category_' +
str(i) + '.txt',
dtype=int)
#归一化
# features /= np.max(features)
#收集每个维度的可选数目，0代表连续值
attri_option_nums = []
x_temp = []
y_temp = []
length = len(subjects)
for j in range(length):
x_temp.append([])
x_temp[j].append(videos[j])
attri_option_nums.append(video_type_num)
x_temp[j].extend(va_labels[j])
attri_option_nums.extend(valabel_type_num)
if 'HCI' in root_dir:
x_temp[j].append(emotions[j])
attri_option_nums.append(emotion_type_num)
x_temp[j].extend(features[j])
attri_option_nums.extend([0] * feature_dim)
y_temp.append(subjects[j] - 1)
x.append(x_temp)
y.append(y_temp)
cvscores = []
#交叉验证
for i in range(splits_num):
x_train = []
y_train = []
x_test = []
y_test = []
for j in range(splits_num):
if j == i:
x_test.extend(x[j])
y_test.extend(y[j])
else:
x_train.extend(x[j])
y_train.extend(y[j])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
model = Bayes.Mix_NB()
model.fit(x_train, y_train, attri_option_nums)
score = model.score(x_test, y_test)
# temp = model.predict_log_proba(x_test)
print(" %s: %.2f%%" % ('acc', score * 100))
cvscores.append(score * 100)
# model = GaussianNB()
# model.fit(x_train,y_train)
# score = model.score(x_test,y_test)
# print(" %s: %.2f%%" % ('acc', score*100))
# cvscores.append(score * 100)
average_score = sum(cvscores) / len(cvscores)
write_score(cvscores, average_score,
root_dir + 'NB/subject_id_cvscores.txt')
return average_score
