def train_dataset_feature_inter(train_dir='train1', subject_list=['subject_1'], feature_type='TD4',
dataset='data1', fold_pre='250_100', z_score=False, channel_pos_list=['O'],
action_num=11, chan_num=4):
my_clfs = ["LDA"]
start_time = time.time()
if feature_type == 'TD4':
feat_num = 4
elif feature_type == 'TD5':
feat_num = 5
chan_len = feat_num * chan_num
norm = ''
channel_pos_list = channel_pos_list[1:]
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub, feature_type, action_num)
if z_score:
trains = data_normalize(trains)
norm = '_norm'
trains_inter = trains[:, 0:chan_len]
tests_inter = trains[:,chan_len:]
training_lda_TD4_inter(
my_clfs, trains_inter, tests_inter, classes,
log_fold=fold_pre + '/' + feature_type + '_' + dataset + '_' + sub + norm,
pos_list=channel_pos_list, num=1, chan_len=chan_len, action_num=action_num)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature(train_dir='train1',
subject_list=['subject_1'],
type='TD4',
dataset='data1',
fold_pre='250_100',
z_score=False):
# my_clfs = ["LDA", "SVC_linear", "SVC_rbf", "Logistic", "QDA", "GaussianNB"]
# my_clfs = ["LDA", "QDA", "GaussianNB", "SVC_linear", "SVC_rbf", "Logistic"]
my_clfs = ["LDA"]
start_time = time.time()
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub)
if z_score:
trains = data_normalize(trains)
sub = 'norm_' + sub
# print trains.mean(axis=0), trains.std(axis=0)
# sys.exit(0)
classifier.training_lda_TD4(my_clfs,
trains,
classes,
log_fold=fold_pre + '/' + type + '_' +
dataset + '_' + sub,
num=1)
#
# classifier.training_lda_TD4_cross(my_clfs, trains1, classes1, trains2, classes2, log_fold = 'TD4_data4_'+subject+'_1to2', num=1)
# classifier.training_lda_TD4_cross(my_clfs, trains2, classes2, trains1, classes1, log_fold = 'TD4_data4_'+subject+'_2to1', num=1)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature_intra(
train_dir='train1', subject_list=['subject_1'], feature_type='TD4', dataset='data1',
fold_pre='250_100', z_score=False, channel_pos_list=['O'], action_num=7, chan_num=4):
print 'train_dataset_feature_intra......'
my_clfs = ["LDA"]
start_time = time.time()
if feature_type == 'TD4':
feat_num = 4
elif feature_type == 'TD5':
feat_num = 5
chan_len = feat_num * chan_num
# 0均值标准化标志
norm = ''
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub, feature_type, action_num)
# 是否进行0均值标准化
if z_score:
trains = data_normalize(trains)
norm = '_norm'
classifier_lda.training_lda_TD4_intra(
my_clfs, trains, classes,
log_fold=fold_pre + '/' + feature_type + '_' + dataset + '_' + sub + norm,
pos_list=channel_pos_list, num=1, chan_len=chan_len,action_num=action_num,
feature_type=feature_type,chan_num=chan_num)
print "Total times: ", time.time() - start_time, 's'
def feature_action_sensitivity(feature_type='TD4'):
''' 对每个特征，分析其在不移位和移位情况下的协方差 '''
results = []
subjects = ['subject_' + str(i + 1) for i in range(5)]
# print subjects
# sys.exit(0)
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(7)]
action_num = len(actions) # 7 动作类型个数
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
group_span = group_num*feat_num
# print group_span
action_span = feat_num*group_num # 16
# print groups, channel_num, channel_span, feat_num
train_dir = 'train4_250_100'
results.append(['subject', 'action', 'feature', 'group', 'means_shift', 'std_shift'] )
plsca = PLSCanonical(n_components=2)
# pos = 1
k=0
for pos_idx, pos_name in enumerate(channel_pos_list[1:]):
pos = pos_idx+1
for subject in subjects:
# shift_simulation = np.ones((action_num,action_span,2))
trains, classes = data_load.load_feature_dataset(train_dir, subject, feature_type)
# m = trains.shape[0]
# print trains.shape, classes.shape, m
# print group_span, group_span*2
# sys.exit(0)
# m = trains.shape[0]*2/3
m = trains.shape[0]/2
X_train = trains[:m, group_span*pos: group_span*(pos+1)]
Y_train = trains[:m:, :group_span]
X_test = trains[m:, group_span*pos: group_span*(pos+1)]
Y_test = trains[m:, :group_span]
plsca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
filename=subject+'_'+pos_name
# plot_plsc_figure(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
plot_plsc_figure_two(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
def train_dataset_feature_inter(
train_dir='train1', subject_list=['subject_1'], type='TD4',
dataset='data1', fold_pre='250_100', z_score=False, channel_pos_list=['O']):
my_clfs = ["LDA"]
start_time = time.time()
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub)
# 选取15个动作中的前六个动作, 临时
# print trains.shape, classes.shape, trains.shape[0]/15*6
len_temp = trains.shape[0] / 15 * 6
trains = trains[0:len_temp, :]
classes = classes[0:len_temp]
# print trains.shape, classes.shape
# sys.exit(0)
if z_score:
trains = data_normalize(trains)
sub = 'norm_' + sub
if dataset == 'data1':
chan_span = 16 # 跨度
chan_num = 2
elif dataset == 'dataset4':
chan_span = 0 # 跨度
chan_num = 4
if type == 'TD4':
feat_num = 4
chan_len = feat_num * chan_span
tests_inter = np.array([])
for idx, channel_pos in enumerate(channel_pos_list):
start = idx*feat_num
if idx == 0:
trains_inter = np.concatenate(
(trains[:, start: start + feat_num], trains[:, start+chan_len:start+chan_len+feat_num]),
axis=1)
elif idx!=0:
test_temp = np.concatenate(
(trains[:, start: start + feat_num], trains[:, start+chan_len:start+chan_len+feat_num]),
axis=1)
if tests_inter.shape[0] != 0:
tests_inter = np.concatenate( (tests_inter, test_temp), axis=1)
else:
tests_inter = test_temp
# print trains_inter.shape, tests_inter.shape
# if idx == 5:
# sys.exit(0)
channel_pos_list = channel_pos_list[1:]
# print channel_pos_list
# print trains_inter.shape, tests_inter.shape
# sys.exit(0)
classifier.training_lda_TD4_inter(
my_clfs, trains_inter, tests_inter, classes,
log_fold=fold_pre + '/' + type + '_' + dataset + '_' + sub + '_updated',
pos_list=channel_pos_list, num=1, chan_len=chan_num*feat_num)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature_inter(train_dir='train4_250_100',
subject_list=['subject_1'],
feature_type='TD4',
dataset='data4',
fold_pre='250_100',
z_score=False,
channel_pos_list=['S0'],
action_num=7,
chan_num=4):
my_clfs = ["LDA"]
start_time = time.time()
channel_pos_list_shift = channel_pos_list[1:]
if feature_type == 'TD4':
feat_num = 4 # 特征维度 TD4:4
elif feature_type == 'TD5':
feat_num = 5
chan_len = feat_num * chan_num # 16
norm = ''
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(
train_dir, sub, feature_type, action_num)
if z_score:
trains = data_normalize(trains)
norm = '_norm'
trains_S0 = trains[:, 0:chan_len]
trains_shift = trains[:, chan_len:]
num = 1
# 生成CCA映射矩阵
# classifier_lda_cca.generate_transform_equations(
# trains_S0, trains_shift, pos_list=channel_pos_list_shift, chan_len=chan_len, subject=sub)
# 进行中心训练策略
classifier_lda_cca.training_lda_TD4_inter(
my_clfs,
trains_S0,
trains_shift,
classes,
log_fold=fold_pre + '/' + feature_type + '_' + dataset + '_' +
sub + norm,
pos_list=channel_pos_list_shift,
chan_len=chan_len,
chan_num=chan_num,
feature_type=feature_type,
action_num=action_num,
num=num,
subject=sub)
print "Total times: ", time.time() - start_time, 's'
def feature_action_sensitivity(feature_type='TD4'):
''' 对每个特征，分析其在不移位和移位情况下的协方差 '''
results = []
subjects = ['subject_' + str(i + 1) for i in range(1)]
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(7)]
action_num = len(actions) # 7 动作类型个数
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
group_span = group_num*feat_num
# print group_span
action_span = feat_num*group_num # 16
# print groups, channel_num, channel_span, feat_num
train_dir = 'train4_250_100'
results.append(['subject', 'action', 'feature', 'group', 'means_shift', 'std_shift'] )
plsca = PLSCanonical(n_components=2)
# pos = 1
k=0
for pos_idx, pos_name in enumerate(channel_pos_list[1:]):
pos = pos_idx+1
for subject in subjects:
# shift_simulation = np.ones((action_num,action_span,2))
trains, classes = data_load.load_feature_dataset(train_dir, subject, feature_type)
# m = trains.shape[0]
# print trains.shape, classes.shape, m
# print group_span, group_span*2
# sys.exit(0)
# m = trains.shape[0]*2/3
m = trains.shape[0]/2
X_train = trains[:m, group_span*pos: group_span*(pos+1)]
Y_train = trains[:m:, :group_span]
X_test = trains[m:, group_span*pos: group_span*(pos+1)]
Y_test = trains[m:, :group_span]
plsca.fit(X_train, Y_train)
X_train_r, Y_train_r = plsca.transform(X_train, Y_train)
X_test_r, Y_test_r = plsca.transform(X_test, Y_test)
filename=subject+'_'+pos_name
# plot_plsc_figure(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
plot_plsc_figure_two(X_train_r,Y_train_r,X_test_r, Y_test_r, filename)
def feature_action_sensitivity(feature_type='TD4'):
''' 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(7)]
action_num = len(actions) # 7 动作类型个数
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
# print groups, channel_num, channel_span, feat_num
train_dir = 'train4_250_100'
results.append(['subject', 'action', 'feature', 'group','means_shift', 'std_shift'] )
for subject in subjects:
shift_simulation = np.ones((action_num,action_span,2))
trains, classes = data_load.load_feature_dataset(train_dir, subject, feature_type)
gaussion_distribute = np.ones( (len(actions), len(groups), len(feature_list), 2))
for action in actions:
trains_action = trains[classes == action]
means = np.mean(trains_action, axis=0)
stds = np.std(trains_action, axis=0)
for group in groups:
for feat_idx, feat_name in enumerate(feature_list):
idx_S0 = (group-1)*feat_num+feat_idx
idx = np.array([(i+1)*action_span+feat_idx+(group-1)*feat_num
for i in range(pos_num-1)])
means_shift = (np.mean(means[idx]) - np.mean(means[idx_S0]))/np.mean(means[idx_S0])
std_shift = (np.std(stds[idx]) - np.std(stds[idx_S0]))
shift_simulation[action-1, (group-1)*feat_num+feat_idx, :] = np.array([means_shift, std_shift])
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
results.append([subject, str(action), feat_name, str(group), str(means_shift), str(std_shift)])
gaussion_distribute[action-1, group-1, feat_idx,:] = [means_shift, std_shift]
# print subject, action, feat_name, group, means_shift[:]
log_result(gaussion_distribute, root_path + '/result_gaussian/sensitivity/'+subject+'_simulation_1', 2)
log_result(results, root_path + '/result_gaussian/sensitivity/feature_action_sensitivity_5', 2)
def train_dataset_feature_inter(
train_dir="train1",
subject_list=["subject_1"],
feature_type="TD4",
dataset="data1",
fold_pre="250_100",
z_score=False,
channel_pos_list=["O"],
action_num=11,
chan_num=4,
):
my_clfs = ["LDA"]
start_time = time.time()
if feature_type == "TD4":
feat_num = 4
elif feature_type == "TD5":
feat_num = 5
chan_len = feat_num * chan_num
norm = ""
channel_pos_list = channel_pos_list[1:]
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub, feature_type, action_num)
if z_score:
trains = data_normalize(trains)
norm = "_norm"
trains_inter = trains[:, 0:chan_len]
tests_inter = trains[:, chan_len:]
training_lda_TD4_inter(
my_clfs,
trains_inter,
tests_inter,
classes,
log_fold=fold_pre + "/" + feature_type + "_" + dataset + "_" + sub + norm,
pos_list=channel_pos_list,
num=1,
chan_len=chan_len,
action_num=action_num,
)
print "Total times: ", time.time() - start_time, "s"
def train_dataset_feature_intra(train_dir='train1',
subject_list=['subject_1'],
feature_type='TD4',
dataset='data1',
fold_pre='250_100',
z_score=False,
channel_pos_list=['O'],
action_num=7,
chan_num=4):
print 'train_dataset_feature_intra......'
my_clfs = ["LDA"]
start_time = time.time()
if feature_type == 'TD4':
feat_num = 4
if feature_type == 'TD5':
feat_num = 5
chan_len = feat_num * chan_num
# 0均值标准化标志
norm = ''
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(
train_dir, sub, feature_type, action_num)
# 是否进行0均值标准化
if z_score:
trains = data_normalize(trains)
norm = '_norm'
classifier_lda_cca.training_lda_TD4_intra(
my_clfs,
trains,
classes,
log_fold=fold_pre + '/' + feature_type + '_' + dataset + '_' +
sub + norm,
pos_list=channel_pos_list,
num=1,
chan_len=chan_len,
action_num=action_num,
feature_type=feature_type,
chan_num=chan_num)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature_intra(
train_dir='train1', subject_list=['subject_1'], type='TD4',
dataset='data1', fold_pre='250_100', z_score=False, channel_pos_list=['O']):
# my_clfs = ["LDA", "SVC_linear", "SVC_rbf", "Logistic", "QDA", "GaussianNB"]
# my_clfs = ["LDA", "QDA", "GaussianNB", "SVC_linear", "SVC_rbf",
# "Logistic"]
my_clfs = ["LDA"]
start_time = time.time()
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub)
if z_score:
trains = data_normalize(trains)
sub = 'norm_' + sub
if dataset == 'data1':
chan_span = 17 # 跨度
elif dataset == 'dataset4':
chan_span = 0 # 跨度,
if type == 'TD4':
feat_num = 4
chan_len = feat_num * chan_span
for idx, channel_pos in enumerate(channel_pos_list):
start = idx*feat_num
# print start, start+feat_num, start+chan_len, start+feat_num+chan_len
trains_intra = np.concatenate(
(trains[:, start: start + feat_num], trains[:, start+chan_len:start+chan_len+feat_num]),
axis=1)
# print trains_intra.shape
# if idx == 1 :
# sys.exit(0)
# print 'Trains and classes: ', trains_intra.shape, classes.shape, idx * chan_len, idx * chan_len + chan_len
classifier.training_lda_TD4_intra(
my_clfs, trains, classes,
log_fold=fold_pre + '/' + type + '_' + dataset + '_' + sub + '_updated',
log_file_pos=channel_pos, num=1)
#
# classifier.training_lda_TD4_cross(my_clfs, trains1, classes1, trains2, classes2, log_fold = 'TD4_data4_'+subject+'_1to2', num=1)
# classifier.training_lda_TD4_cross(my_clfs, trains2, classes2, trains1, classes1, log_fold = 'TD4_data4_'+subject+'_2to1', num=1)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature(train_dir='train1', subject_list=['subject_1'], type='TD4', dataset='data1', fold_pre='250_100', z_score=False):
# my_clfs = ["LDA", "SVC_linear", "SVC_rbf", "Logistic", "QDA", "GaussianNB"]
# my_clfs = ["LDA", "QDA", "GaussianNB", "SVC_linear", "SVC_rbf", "Logistic"]
my_clfs = ["LDA"]
start_time = time.time()
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub)
if z_score:
trains = data_normalize(trains)
sub = 'norm_' + sub
# print trains.mean(axis=0), trains.std(axis=0)
# sys.exit(0)
classifier.training_lda_TD4(
my_clfs, trains, classes, log_fold=fold_pre + '/' + type + '_' + dataset + '_' + sub, num=1)
#
# classifier.training_lda_TD4_cross(my_clfs, trains1, classes1, trains2, classes2, log_fold = 'TD4_data4_'+subject+'_1to2', num=1)
# classifier.training_lda_TD4_cross(my_clfs, trains2, classes2, trains1, classes1, log_fold = 'TD4_data4_'+subject+'_2to1', num=1)
print "Total times: ", time.time() - start_time, 's'
def train_dataset_feature_inter(
train_dir='train4_250_100', subject_list=['subject_1'], feature_type='TD4', dataset='data4',
fold_pre='250_100', z_score=False, channel_pos_list=['S0'], action_num=7, group_num=4):
my_clfs = ["LDA"]
start_time = time.time()
# channel_pos_list_shift = channel_pos_list[1:]
channel_pos_list_shift = channel_pos_list
action_num = 7
group_num = 4
if feature_type == 'TD4':
feat_num = 4 # 特征维度 TD4:4
elif feature_type == 'TD5':
feat_num = 5
chan_num = 4 # 通道个数，4通道
chan_len = feat_num * chan_num # 16
for sub in subject_list:
trains, classes = data_load.load_feature_dataset(train_dir, sub, feature_type)
tests_inter = np.array([])
trains_inter = trains[:, 0:chan_len]
trains_simu, classes_simu = guassion_simu(trains_inter, classes, sub, action_num, chan_num, feat_num)
# trains_simu, classes_simu = trains_inter, classes
tests_inter = trains
if z_score:
trains_simu = data_normalize(trains_simu)
tests_inter = data_normalize(tests_inter)
sub = 'norm_' + sub
num = 3
classifier_lda.training_lda_TD4_inter(
my_clfs, trains_simu, classes_simu, tests_inter, classes,
log_fold=fold_pre + '/' + feature_type + '_' + dataset + '_' + sub + '_simu1',
pos_list=channel_pos_list_shift, chan_len=chan_len, group_num=group_num,
feature_type=feature_type, action_num=action_num, num=num)
print "Total times: ", time.time() - start_time, 's'
def feature_action_sensitivity(feature_type='TD4'):
''' 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(7)]
action_num = len(actions) # 7 动作类型个数
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
# print groups, channel_num, channel_span, feat_num
train_dir = 'train4_250_100'
results.append(
['subject', 'action', 'feature', 'group', 'means_shift', 'std_shift'])
for subject in subjects:
shift_simulation = np.ones((action_num, action_span, 2))
trains, classes = data_load.load_feature_dataset(
train_dir, subject, feature_type)
gaussion_distribute = np.ones(
(len(actions), len(groups), len(feature_list), 2))
for action in actions:
trains_action = trains[classes == action]
means = np.mean(trains_action, axis=0)
stds = np.std(trains_action, axis=0)
for group in groups:
for feat_idx, feat_name in enumerate(feature_list):
idx_S0 = (group - 1) * feat_num + feat_idx
idx = np.array([(i + 1) * action_span + feat_idx +
(group - 1) * feat_num
for i in range(pos_num - 1)])
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0]
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
# results.append([subject, str(action), feat_name, str(group)] + map(str, means_shift))
means_shift = np.mean(means[idx]) - np.mean(means[idx_S0])
std_shift = np.std(stds[idx]) - np.std(stds[idx_S0])
shift_simulation[action - 1, (group - 1) * feat_num +
feat_idx, :] = np.array(
[means_shift, std_shift])
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
results.append([
subject,
str(action), feat_name,
str(group),
str(means_shift),
str(std_shift)
])
gaussion_distribute[action - 1, group - 1, feat_idx, :] = [
means_shift, std_shift
]
# print subject, action, feat_name, group, means_shift[:]
log_result(
gaussion_distribute,
root_path + '/result/sensitivity/' + subject + '_simulation_1', 2)
log_result(results,
root_path + '/result/sensitivity/feature_action_sensitivity_5',
2)
def feature_action_sensitivity(feature_type="TD4"):
""" 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(7)]
action_num = len(actions) # 7 动作类型个数
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
# print groups, channel_num, channel_span, feat_num
train_dir = "train4_250_100"
results.append(["subject", "action", "feature", "group", "means_shift", "std_shift"])
for subject in subjects:
shift_simulation = np.ones((action_num, action_span, 2))
trains, classes = data_load.load_feature_dataset(train_dir, subject, feature_type)
gaussion_distribute = np.ones((len(actions), len(groups), len(feature_list), 2))
for action in actions:
trains_action = trains[classes == action]
means = np.mean(trains_action, axis=0)
stds = np.std(trains_action, axis=0)
for group in groups:
for feat_idx, feat_name in enumerate(feature_list):
idx_S0 = (group - 1) * feat_num + feat_idx
idx = np.array(
[(i + 1) * action_span + feat_idx + (group - 1) * feat_num for i in range(pos_num - 1)]
)
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0]
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
# results.append([subject, str(action), feat_name, str(group)] + map(str, means_shift))
means_shift = np.mean(means[idx]) - np.mean(means[idx_S0])
std_shift = np.std(stds[idx]) - np.std(stds[idx_S0])
shift_simulation[action - 1, (group - 1) * feat_num + feat_idx, :] = np.array(
[means_shift, std_shift]
)
# means_shift = abs(means[idx] - means[idx_S0])/means[idx_S0] \
# + abs(stds[idx]-stds[idx_S0])/stds[idx_S0]
results.append([subject, str(action), feat_name, str(group), str(means_shift), str(std_shift)])
gaussion_distribute[action - 1, group - 1, feat_idx, :] = [means_shift, std_shift]
# print subject, action, feat_name, group, means_shift[:]
log_result(gaussion_distribute, root_path + "/result/sensitivity/" + subject + "_simulation_1", 2)
log_result(results, root_path + "/result/sensitivity/feature_action_sensitivity_5", 2)