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 feature_action_sensitivity(featuret_type="TD4", action_num=7):
""" 对每个特征，分析其在不移位和移位情况下的差异性 """
results = []
subjects = ["subject_" + str(i + 1) for i in range(5)]
channel_pos_list = ["S0", "U1", "U2", "D1", "D2", "L1", "L2", "R1", "R2"] # 中心位置 # 上 下 左 右
pos_num = len(channel_pos_list)
actions = [i + 1 for i in range(action_num)]
groups = [i + 1 for i in range(4)]
if feature_type == "TD4":
feature_list = ["MAV", "ZC", "SSC", "WL"]
elif feature_type == "TD5":
feature_list = ["MAV", "ZC", "SSC", "WL", "RMS"]
feat_num = len(feature_list) # 4 特征维度
groups = [i + 1 for i in range(4)]
group_num = len(groups) # 4 通道数
action_span = feat_num * group_num # 16
train_dir = "train4_250_100"
results.append(["subject", "action", "feature", "group"] + channel_pos_list[1:])
for subject in subjects:
for action in actions:
filename = subject + "_feat_" + featuret_type + "_action_" + str(action)
data = np.load(root_path + "/train4_250_100/" + filename + ".npy")
means = np.mean(data, axis=0)
stds = np.std(data, axis=0)
for group in groups:
for feat_idx, feat_name in enumerate(feature_list):
idx = np.array(
[(i + 1) * action_span + feat_idx + (group - 1) * feat_num for i in range(pos_num - 1)]
)
idx_S0 = (group - 1) * feat_num + feat_idx
# print idx, idx_S0
# variation = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
variation = abs(means[idx] - means[idx_S0]) / means[idx_S0]
results.append([subject, str(action), feat_name, str(group)] + map(str, variation))
# print results
# print subject, action, feat_name, group, variation[:]
fold_path = root_path + "/result_gaussian/sensitivity/"
new_fold(fold_path)
log_result(results, fold_path + "feature_action_sensitivity", 2)
def feature_extract(raw_data, target, window, overlap, sample_rate, feature_type='TD4', out_dir='train1', subject='subject_1', feat_num=4):
''' 生成数据样本集合（提取特征），参数：原始数据，类别，时间窗（250），重叠窗（100），采样率（1024）'''
# print raw_data.shape, target, window, overlap
print "----feature_extract target ", target, "...................."
start_time = time.time()
winsize = (int)(sample_rate * window * 0.001)
incsize = (int)(sample_rate * overlap * 0.001)
start = 0
# print winsize, incsize
x_dim = (raw_data.shape[0] - winsize) / incsize + 1
y_dim = raw_data.shape[1] * feat_num
index = 0
trains = np.zeros((x_dim, y_dim))
while start + winsize < raw_data.shape[0]:
train = np.array([])
for i in range(raw_data.shape[1]):
if feature_type == 'TD4':
cur_win_feature = fe.extract_TD4(
raw_data[start:start + winsize, i]) # 提取TD4（MAV，ZC，SSC，WL）四种时域特征
if feature_type == 'TD5':
cur_win_feature = fe.extract_TD5(
raw_data[start:start + winsize, i]) # 提取TD4（MAV，ZC，SSC，WL）四种时域特征
# elif feature_type:
# cur_win_feature = fe.extract_TD4_AR(raw_data[start:start+winsize,
# i]) # 提取TD4（MAV，ZC，SSC，WL）四种时域特征+AR特征
train = np.concatenate(
(train, cur_win_feature), axis=None) # 对每个时间窗提取特征
# print train.shape
trains[index] = train
index += 1
start += incsize
log_file = root_path + '/' + out_dir + '/' + \
subject + '_feat_' + feature_type + '_action_' + str(target)
if(not new_fold(root_path + '/' + out_dir)):
sys.exit(1)
np.save(log_file + '.npy', trains)
# np.savetxt(log_file + '.csv', trains, fmt="%s", delimiter=",")
print "----feature_extract target ", target, " over, time elapsed:", time.time() - start_time
def feature_action_sensitivity(featuret_type='TD4', action_num=7):
''' 对每个特征，分析其在不移位和移位情况下的差异性 '''
results = []
subjects = ['subject_' + str(i + 1) for i in range(5)]
channel_pos_list = [
'S0', # 中心位置
'U1',
'U2',
'D1',
'D2',
'L1',
'L2',
'R1',
'R2'
] # 上 下 左 右
pos_num = len(channel_pos_list)
actions = [i + 1 for i in range(action_num)]
groups = [i + 1 for i in range(4)]
if feature_type == 'TD4':
feature_list = ['MAV', 'ZC', 'SSC', 'WL']
elif feature_type == 'TD5':
feature_list = ['MAV', 'ZC', 'SSC', 'WL', 'RMS']
feat_num = len(feature_list) # 4 特征维度
groups = [i + 1 for i in range(4)]
group_num = len(groups) # 4 通道数
action_span = feat_num * group_num # 16
train_dir = 'train4_250_100'
results.append(['subject', 'action', 'feature', 'group'] +
channel_pos_list[1:])
for subject in subjects:
for action in actions:
filename = subject + '_feat_' + featuret_type + '_action_' + str(
action)
data = np.load(root_path + '/train4_250_100/' + filename + '.npy')
means = np.mean(data, axis=0)
stds = np.std(data, axis=0)
for group in groups:
for feat_idx, feat_name in enumerate(feature_list):
idx = np.array([(i + 1) * action_span + feat_idx +
(group - 1) * feat_num
for i in range(pos_num - 1)])
idx_S0 = (group - 1) * feat_num + feat_idx
# print idx, idx_S0
# variation = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
variation = abs(means[idx] - means[idx_S0]) / means[idx_S0]
results.append(
[subject, str(action), feat_name,
str(group)] + map(str, variation))
# print results
# print subject, action, feat_name, group, variation[:]
fold_path = root_path + '/result_gaussian/sensitivity/'
new_fold(fold_path)
log_result(results, fold_path + 'feature_action_sensitivity', 2)
