import numpy as np 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)将上面的代码在成本矩阵上加入一个时间权重,时间权重要考虑时间序列的
时间: 2023-06-15 11:07:30 浏览: 72
变化趋势,可以将时间权重加入到每个代价值的计算中。修改后的代码如下:
import numpy as np
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]) + abs(i - j) # 添加时间权重,考虑时间序列的变化趋势
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)