class CardiogramDataset(Dataset):
def __init__(self, list_files, labels):
self.helper = DataHelper()
self.list_files = list_files # list_files is the filename of training sample
self.labels = labels
def __getitem__(self, index):
features = self.helper.get_features_from_txt(self.list_files[index])
features = torch.FloatTensor(features)
# print("index {} label {}".format(index, self.labels[index]))
label = torch.FloatTensor([self.labels[index]])
# print("float label {}".format(label))
return features, label
def __len__(self):
return len(self.list_files)
if type(m)==nn.Linear or type(m)==nn.Conv2d:
torch.nn.init.xavier_normal_(m.weight)
device = torch.device('cuda:1')
net.apply(init_weights)
net.to(device)
def get_files():
"""
:return:
"""
train_path = Constant.TRAIN_DATA_PATH
files = [f for f in os.listdir(train_path) if os.path.isfile(os.path.join(train_path, f))]
return files
helper = DataHelper()
files = helper.files
num_labels = helper.get_num_label_i(0) ## indicator patient has the 0th arrythmia or not
# for file, num_label_0 in zip(files, num_labels):
# print(file+"\t"+str(num_label_0))
datas, labels = k_fold(5, files, num_labels)
validation_data = datas[0]
validation_label = labels[0]
train_data = []
train_labels = []
for i in range(1, 5):
train_data += datas[i]
class LSTM_RESHAPE(nn.Module):
def forward(self, x):
return x.view((-1, x.shape[2], x.shape[1]))
class LSTM(nn.Module):
def __init__(self):
super(LSTM, self).__init__()
self.lstm = nn.Sequential(LSTM_RESHAPE(), )
def forward(self, x):
return x
helper = DataHelper()
train_iter, test_iter = helper.get_train_and_validation_iter()
for X, y in train_iter:
x = X
break
# print(x.shape)
# lstm_reshape = LSTM_RESHAPE()
# x = lstm_reshape(x)
# print(x.shape)
#
#
# fcn = FCN()
# x = fcn(x)
# print("x.shape: {}".format(x.shape))
def __init__(self, list_files, labels):
self.helper = DataHelper()
self.list_files = list_files # list_files is the filename of training sample
self.labels = labels
for j in range(fold_num):
r = random.randint(0, len(list_files) - 1)
tmp_datas.append(list_files[r])
tmp_labels.append(labels[r])
del list_files[r]
del labels[r]
k_datas.append(tmp_datas)
k_labels.append(tmp_labels)
k_datas.append(list_files)
k_labels.append(labels)
return k_datas, k_labels
helper = DataHelper()
files = helper.files
num_labels = helper.num_labels
datas, labels = k_fold(5, files, num_labels)
validation_data = datas[0]
validation_label = labels[0]
train_data = []
train_labels = []
for i in range(1, 5):
train_data += datas[i]
train_labels += labels[i]
dataset = CardiogramDataset(train_data, train_labels)
# print(len(dataset))