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