[{"title":"( 15 个子文件 24.33MB ) 在Python中的轨迹分析和熊猫学习套件的学习方法:形成点集的轨迹基于Grid表示形式将轨迹建模为字符串.Benchmarked KNN,Random Forest,Logistic回归分类算法可对轨迹进行有效分类","children":[{"title":"Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn-master","children":[{"title":"test_set.csv <span style='color:#111;'> 48.97MB </span>","children":null,"spread":false},{"title":"best_classifier.py <span style='color:#111;'> 2.10KB </span>","children":null,"spread":false},{"title":"test_set_a2.csv <span style='color:#111;'> 19.11KB </span>","children":null,"spread":false},{"title":"down_left_point.py <span style='color:#111;'> 1.28KB </span>","children":null,"spread":false},{"title":"test_set_a1.csv <span style='color:#111;'> 26.87KB </span>","children":null,"spread":false},{"title":"auxiliaryfunctions.py <span style='color:#111;'> 3.64KB </span>","children":null,"spread":false},{"title":"grid_points.py <span style='color:#111;'> 1.49KB </span>","children":null,"spread":false},{"title":"classifytraj.py <span style='color:#111;'> 2.38KB </span>","children":null,"spread":false},{"title":"lcss_neighbors.py <span style='color:#111;'> 1.83KB </span>","children":null,"spread":false},{"title":"train_set.csv.zip <span style='color:#111;'> 16.04MB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 78B </span>","children":null,"spread":false},{"title":"fast_dtw_neigbors.py <span style='color:#111;'> 1.70KB </span>","children":null,"spread":false},{"title":"LICENSE.md <span style='color:#111;'> 11.09KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.77KB </span>","children":null,"spread":false},{"title":"datacleaning.py <span style='color:#111;'> 3.07KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]