DTW 算法优化

import numpy as np
#优化前
def Dtw(a, b):
    dis = np.full((len(a) + 1, len(b) + 1), np.inf)
    dis[0, 0] = 0
    for i in range(0, len(a)):
        for j in range(0, len(b)):
            print(a[i],b[j])
            dis[i + 1, j + 1] = (a[i] - b[j]) ** 2
    for i in range(1, len(a) + 1):
        for j in range(1, len(b) + 1):
            dis[i, j] = min(dis[i - 1, j - 1], dis[i, j - 1], dis[i - 1, j]) + dis[i, j]
    result = dis[len(a)-1, len(b)-1] / (len(a) + len(b))
    return result
#优化后
def Dtw_k(a, b):

    dis = np.full((len(a) + 1, len(b) + 1), np.inf)
    dis[0, 0] = 0
    x=np.array(a).repeat(len(b),axis=0).reshape(len(a),len(b)).T
    y=np.array(b).repeat(len(a),axis=0).reshape(len(b),len(a))
    # for i in range(0, len(a)):
    #     for j in range(0, len(b)):
    #         print(a[i],b[j])
    #         dis[i + 1, j + 1] = (a[i] - b[j]) ** 2
    dis[1:, 1:] = ((x - y) ** 2).T

    for i in range(1, len(a) + 1):
        for j in range(1, len(b) + 1):
            print(dis[i - 1, j - 1], dis[i, j - 1],dis[i - 1, j])

            dis[i, j] = min(dis[i - 1, j - 1], dis[i, j - 1], dis[i - 1, j]) + dis[i, j]
    result = dis[len(a)-1, len(b)-1] / (len(a) + len(b))
    return result
x = [3, 2, 1]
y = [1, 2]
print(Dtw_k(x, y))
if __name__ == '__main__':
    pass