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我正在尝试使用 [https://github.com/jim-spyropoulos/Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-Learn] 1 中的 fast_dtw_neighbors.py 计算轨迹之间的DTW相似 性当将轨迹作为参数提供给 fastdtw 函数时，就像 .py 文件一样：

# %load fast_dtw_neigbors.py
import csv
import os
import re

import numpy as np
import pandas as pd
from fastdtw import fastdtw

from auxiliaryfunctions import *

df = pd.read_pickle('./final_cleaned.df')
# l = df['timestamp_longitude_latitude'].iloc[0]
with open('./test_set_a1.csv', 'r') as f:
    reader = csv.reader(f)
    data = (list(rec) for rec in csv.reader(f, delimiter=','))
    i = 0
    count = 0
    trajectories_read = []  # list with all trajectories read
    for row in data:
        if (i != 0):
            tmp_list = []  # to read triples
            trajectory = []  # here will be the final trajectory
            j = 0
            while (j != len(row)):
                tmp_list = [float(re.sub('[[]', '', row[j])), float(row[j + 1]), float(re.sub('[]]', '', row[j + 2]))]
                j = j + 3
                trajectory.append(tmp_list)
                tmp_list = []
            trajectories_read.append(trajectory)
        i = i + 1

print("Dataset read successfully.")


j = 0

for traj in trajectories_read:

    # computeDTW similarity of given trajectory with all trajectories of the cleaned_dataset

    distances = []
    for elem in df['timestamp_longitude_latitude']:

        distance, path = fastdtw(elem, traj, dist=haversine_np)
        distances.append(distance)
    # find the 5 smaller using np arrays
    distancesnp = np.array(distances)
    sorted_ind = np.argsort(distancesnp)[:5]
    # for each we print the results
    k = 0
    for elem in sorted_ind:
        print_results(df, distancesnp, elem, "./DTWresults/trajectory" + str(j) + "neighbor" + str(k))
        k = k + 1
    j = j + 1

我收到此错误：

TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12280/1160975391.py in <module>
     10     for elem in df['timestamp_longitude_latitude']:
     11 
---> 12         distance, path = fastdtw(elem, traj, dist=haversine_np)
     13         distances.append(distance)
     14     # find the 5 smaller using np arrays

D:\anaconda\envs\geo_env\lib\site-packages\fastdtw.py in fastdtw(x, y, radius, dist)
     15         return dtw(x, y, dist=dist)
     16 
---> 17     x_shrinked = __reduce_by_half(x)
     18     y_shrinked = __reduce_by_half(y)
     19     distance, path = fastdtw(x_shrinked, y_shrinked, radius=radius, dist=dist)

D:\anaconda\envs\geo_env\lib\site-packages\fastdtw.py in __reduce_by_half(x)
     42 
     43 def __reduce_by_half(x):
---> 44     return [(x[i//2] + x[1+i//2]) / 2 for i in xrange(0, len(x), 2)]
     45 
     46 

D:\anaconda\envs\geo_env\lib\site-packages\fastdtw.py in <listcomp>(.0)
     42 
     43 def __reduce_by_half(x):
---> 44     return [(x[i//2] + x[1+i//2]) / 2 for i in xrange(0, len(x), 2)]
     45 
     46 

TypeError: unsupported operand type(s) for /: 'list' and 'int'

我使用 type() 检查了traj和elem类型，它们都是列表。我还在 Github 存储库中打开了一个问题，但没有得到任何响应。

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