def result_integrate_intra(time_now):
training_type = 'intra'
span = len(proportional_list)
fold_path = root_path + '/result_test1/proportional_integrate'
new_fold(fold_path)
feature_type = 'TD4'
norm = '_norm'
subject_list = ['subject_' + str(i) for i in range(1, 6)]
res_all = []
blank_line = ['' for i in range(len(channel_pos_list))]
res_all.append(blank_line)
for action in action_lists:
for subject in subject_list:
res = []
index = 2
res_ind = 1
data = result_load('250_100',feature_type, subject, norm, action, training_type)
title = feature_type+'_'+subject+'_action_1-'+str(action)
res_head = [title]
res_head.extend(proportional_list)
res.append(res_head)
for i in range(len(channel_pos_list)):
res_intra = [channel_pos_list[i]]
# print res_intra
res_intra.extend(map(float,data[index:index+span,4][:]))
index += span
res.append(res_intra)
res_np = np.array(res)
res_aver = ['average']
for i in range(len(proportional_list)):
res_aver.append(np.mean(map(float,res_np[res_ind:,i+1])))
res.append(res_aver)
# file_path = fold_path + '/prop_'+training_type+'_'+title+'_'+str(time_now)
# log_result(res, file_path, 2)
res_all.extend(res)
res_all.append(blank_line)
res_all.append(blank_line)
res_all.append(blank_line)
res_all.append(blank_line)
res_all.append(blank_line)
file_path = fold_path + '/prop_'+training_type+'_all_'+str(time_now)
log_result(res_all, file_path, 2)
def result_integrate_intra(time_now):
training_type = 'intra'
feature_type = 'TD4'
norm = '_norm'
channel_pos_list = [
'S0', # 中心位置
'U1',
'U2',
'D1',
'D2',
'L1',
'L2',
'R1',
'R2'
] # 上 下 左 右
# action = 7
# subject='subject_1'
action_lists = [7, 9, 11]
subject_list = ['subject_' + str(i) for i in range(1, 6)]
res_all = []
blank_line = ['' for i in range(len(channel_pos_list) + 1)]
for i in range(5):
res_all.append(blank_line)
fold_path = root_path + '/result/cca_analyse'
new_fold(fold_path)
for action in action_lists:
for subject in subject_list:
data = result_load('250_100', feature_type, subject, norm, action,
training_type)
title = feature_type + '_' + subject + '_action_1-' + str(action)
res = []
res_head = [title]
res_head.extend(channel_pos_list[1:])
res_head.append('Average')
res.append(res_head)
index = 3
span = len(channel_pos_list) - 1
res_intra = ['intra']
temp = map(float, data[index:index + span, 4][:])
res_intra.extend(temp)
res_intra.append(np.mean(temp))
res.append(res_intra)
index += span
res_center = ['center']
temp = map(float, data[index:index + span, 4][:])
res_center.extend(temp)
res_center.append(np.mean(temp))
res.append(res_center)
index += span
res_group = ['group']
temp = map(float, data[index:index + span, 4][:])
res_group.extend(temp)
res_group.append(np.mean(temp))
res.append(res_group)
for i in range(6):
n_components = 6 + i * 2
index += span
res_CCA = ['CCA_' + str(n_components)]
temp = map(float, data[index:index + span, 4][:])
res_CCA.extend(temp)
res_CCA.append(np.mean(temp))
res.append(res_CCA)
res_all.extend(res)
for j in range(10):
res_all.append(blank_line)
file_path = fold_path + '/' + feature_type + '_' + training_type + '_' + time_now
log_result(res_all, file_path, 2)
def result_integrate_intra(time_now):
training_type = 'intra'
feature_type = 'TD4'
norm = '_norm'
channel_pos_list = ['S0', # 中心位置
'U1', 'U2', 'D1', 'D2', 'L1', 'L2', 'R1', 'R2'] # 上 下 左 右
# action = 7
# subject='subject_1'
action_lists = [7, 9, 11]
subject_list = ['subject_' + str(i) for i in range(1, 6)]
res_all = []
blank_line = ['' for i in range(len(channel_pos_list)+1)]
for i in range(5):
res_all.append(blank_line)
fold_path = root_path + '/result/cca_analyse'
new_fold(fold_path)
for action in action_lists:
for subject in subject_list:
data = result_load('250_100',feature_type, subject, norm, action, training_type)
title = feature_type+'_'+subject+'_action_1-'+str(action)
res = []
res_head = [title]
res_head.extend(channel_pos_list[1:])
res_head.append('Average')
res.append(res_head)
index = 3
span = len(channel_pos_list)-1
res_intra = ['intra']
temp = map(float, data[index:index+span,4][:])
res_intra.extend(temp)
res_intra.append(np.mean(temp))
res.append(res_intra)
index += span
res_center = ['center']
temp = map(float, data[index:index+span,4][:])
res_center.extend(temp)
res_center.append(np.mean(temp))
res.append(res_center)
index += span
res_group = ['group']
temp = map(float, data[index:index+span,4][:])
res_group.extend(temp)
res_group.append(np.mean(temp))
res.append(res_group)
for i in range(6):
n_components = 6+i*2
index += span
res_CCA = ['CCA_'+str(n_components)]
temp = map(float, data[index:index+span,4][:])
res_CCA.extend(temp)
res_CCA.append(np.mean(temp))
res.append(res_CCA)
res_all.extend(res)
for j in range(10):
res_all.append(blank_line)
file_path = fold_path + '/' + feature_type+'_'+training_type+'_'+time_now
log_result(res_all, file_path, 2)