data_processing_code/test.py

#%%
import pandas as pd
import numpy as np
df=pd.read_csv('../Data/72_Trajectory.csv')




# from concurrent.futures import ThreadPoolExecutor
# max_timesteps=296
# feature_num=16
# label_nums=9
#
# ##数据增强，准确版本
# def generate_partial_sequences(row, max_timesteps=max_timesteps, features_per_timestep=feature_num, fill_value=-10):
#     # 确定实际长度
#     actual_length_indices = np.where(row[:-label_nums] != fill_value)[0]  # 排除最后的标签列
#     if len(actual_length_indices) > 0:
#         actual_length = (actual_length_indices[-1] // features_per_timestep) + 1
#     else:
#         actual_length = 0
#     partial_sequences = []
#     step_size = max(1, int(actual_length * 0.1))  # 步长为实际长度的10%，至少为1
#     print(f'{actual_length},{step_size}')
#
#     for end_length in range(step_size, actual_length + step_size, step_size):
#         # 计算结束点，不超过实际长度
#         end_length = min(end_length, actual_length)
#         partial_sequence_list = row[:end_length * features_per_timestep].tolist()
#
#         selected_features_list = [partial_sequence_list[i:i + 10] for i in
#                                   range(0, len(partial_sequence_list), features_per_timestep)]
#         selected_features_list = [item for sublist in selected_features_list for item in sublist]
#
#         hand_rotation_axis = partial_sequence_list[-6:-3]
#         hand_direction = partial_sequence_list[-3:]
#
#         padding_length = (max_timesteps - end_length) * 10  # 计算填充长度
#         selected_features_list.extend([fill_value] * padding_length)  # 添加填充
#
#         selected_features_list.extend(row[-9:])  # 添加标签
#         selected_features_list.extend(hand_rotation_axis)  # 添加HandRotationAxis和HandDirection
#         selected_features_list.extend(hand_direction)
#
#         print(len(selected_features_list))
#
#         partial_sequences.append(selected_features_list)
#     return partial_sequences
#
#
#
# df = pd.read_csv('../Data/testing/test_data_supervised.csv')
# partial_sequences_df = generate_partial_sequences(df.iloc[0])