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 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='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_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 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 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'