def extract_frame_matrix_pre_label(filename, pre_timestep):
# slide window: length = 1s; overlay = 50%
# (samples, steps, data_dims)
raw = pd.read_csv(filename)
raw = pd.DataFrame(raw)
start = 0
end = start + 64
frame_list = list()
y = list()
data_x, data_y = pre.process_dataset_file(np.array(raw))
raw = np.hstack([data_x, np.reshape(data_y, [data_y.shape[0], 1])])
row, _ = raw.shape
while end < row:
sample = raw[start:end, 1:19]
label = raw[end - 1, -1]
y.append(label)
frame_list.append(sample)
start = start + 32
end = start + 64
frame_list = np.array(frame_list)
y = np.array(y)
if len(y) <= pre_timestep:
assert 'wrong timestep'
frame_list = frame_list[0:-pre_timestep, :, :]
y = y[pre_timestep:]
return frame_list, y
def extract_frame_matrix_pre_label(filename, look_back):
# slide window: length = 1s; overlay = 50%
# (samples, steps, data_dims)
raw = pd.read_csv(filename)
raw = pd.DataFrame(raw)
start = 0
end = start + 64
frame_list = list()
y = list()
data_x, data_y = pre.process_dataset_file(np.array(raw))
raw = np.hstack([data_x, np.reshape(data_y, [data_y.shape[0], 1])])
row, _ = raw.shape
while end < row:
sample = raw[start:end, 1:19]
label = raw[end - 1, -1]
y.append(label)
frame_list.append(sample)
start = start + 32
end = start + 64
frame_list = np.array(frame_list)
y = np.array(y)
if len(y) <= look_back:
assert 'wrong timestep'
frame_list = frame_list[0:-look_back, :, :]
yy = np.zeros([frame_list.shape[0]])
for i in range(frame_list.shape[0]):
yy[i] = 1 if 1 in y[i + 1:i + look_back + 1] else 0
return frame_list, yy
def extract_frame_matrix_sequential(filename, timestep):
# slide window: length = 1s; overlay = 50%
# (samples, steps, data_dims)
raw = pd.read_csv(filename)
raw = pd.DataFrame(raw)
data_x, data_y = pre.process_dataset_file(np.array(raw))
raw = np.hstack([data_x, np.reshape(data_y, [data_y.shape[0], 1])])
row, _ = raw.shape
least_length = 64 + (timestep - 1) * 32
samples, lables = spilt_squential(
raw, np.arange(0, raw.shape[0] - least_length, 32), timestep)
# print(np.unique(lables))
return samples, lables
def extract_frame_matrix(filename):
# slide window: length = 1s; overlay = 50%
raw = pd.read_csv(filename, header=None)
raw = pd.DataFrame(raw)
row, col = raw.shape
start = 0
end = start + 64
frame_list = []
y = []
data_x, data_y = pre.process_dataset_file(np.array(raw))
print(data_x.shape, data_y.shape)
# raw = pd.DataFrame(np.hstack([data_x,data_y]))
while end <= row:
sample = raw.iloc[start:end, 1:10]
sample = sample.as_matrix()
label = raw.iloc[end - 1, -1]
y.append(label)
frame_list.extend(sample)
start = start + 32
end = start + 64
return frame_list, y
def extract_frame_concatenate(filename):
# slide window: length = 1s; overlay = 50%
raw = pd.read_csv(filename, header=None)
raw = pd.DataFrame(raw)
row, col = raw.shape
start = 0
end = start + 64
frame_list = list()
y = list()
data_x, data_y = pre.process_dataset_file(np.array(raw))
raw = pd.DataFrame(
np.hstack([data_x, np.reshape(data_y, [data_y.shape[0], 1])]))
while end <= row:
sample = raw.iloc[start:end, 1:19]
sample = sample.as_matrix()
sample = sample.reshape((1, 64 * 18))
label = raw.iloc[end, -1]
y.append(label)
frame_list.extend(sample)
start = start + 32
end = start + 64
return frame_list, y