import numpy as np def mlwf(alpha, beta, t_i, t_j): # 时间权重 g = abs(int(t_i) - int(t_j)) a = -alpha * (g - beta) exp = math.exp(a) omega = 1 / (1 + exp) return omega def dtw(s, t): n, m = len(s), len(t) dtw_matrix = np.zeros((n + 1, m + 1)) for i in range(1, n + 1): dtw_matrix[i, 0] = float('inf') for j in range(1, m + 1): dtw_matrix[0, j] = float('inf') dtw_matrix[0, 0] = 0 for i in range(1, n + 1): for j in range(1, m + 1): cost = abs(s[i - 1] - t[j - 1]) dtw_matrix[i, j] = cost + min(dtw_matrix[i - 1, j], dtw_matrix[i, j - 1], dtw_matrix[i - 1, j - 1]) return dtw_matrix[n, m] def calculate_dtw(seq1, seq2): s = np.array(seq1)[:, 1] t = np.array(seq2)[:, 1] return dtw(s, t) # 示例代码 seq1 = [(1, 10), (2, 20), (3, 30), (4, 40),(5,41)] seq2 = [(1, 15), (2, 32),(3, 25), (4, 35),(5, 49)] dtw_distance = calculate_dtw(seq1, seq2) print('DTW距离:', dtw_distance)帮我修改代码使得上述代码中的t_i和t_j分别为两条时间序列的时间信息

import math
import numpy as np

def mlwf(alpha, beta, t_i, t_j): # 时间权重
g = abs(int(t_i) - int(t_j))
a = -alpha * (g - beta)
exp = math.exp(a)
omega = 1 / (1 + exp)
return omega

def dtw(s, t):
n, m = len(s), len(t)
dtw_matrix = np.zeros((n + 1, m + 1))
for i in range(1, n + 1):
dtw_matrix[i, 0] = float('inf')
for j in range(1, m + 1):
dtw_matrix[0, j] = float('inf')
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(s[i - 1][1] - t[j - 1][1])
time_cost = mlwf(0.01, 1, s[i - 1][0], t[j - 1][0])
dtw_matrix[i, j] = time_cost * cost + min(dtw_matrix[i - 1, j], dtw_matrix[i, j - 1], dtw_matrix[i - 1, j - 1])
return dtw_matrix[n, m]

def calculate_dtw(seq1, seq2):
return dtw(seq1, seq2)

# 示例代码
seq1 = [(1, 10), (2, 20), (3, 30), (4, 40),(5,41)]
seq2 = [(1, 15), (2, 32),(3, 25), (4, 35),(5, 49)]
dtw_distance = calculate_dtw(seq1, seq2)
print('DTW距离:', dtw_distance)

在上述代码中，我将t_i和t_j分别表示为了时间序列中的每个元组的第一个元素，即时间信息。并且在计算dtw距离的时候，我还使用了mlwf函数来计算时间权重。