dtw算法计算相似度python

下面是一个简单的Python代码示例，演示如何使用DTW算法计算两个时间序列之间的相似度。

import numpy as np

def dtw_distance(ts_a, ts_b, d=lambda x,y: abs(x-y), mww=10000):
    """Computes dtw distance between two time series
    
    ts_a, ts_b : array-like, shape = [n_samples]
        Two arrays containing n_samples (x,y) coordinates to be aligned
        using dtw.
    
    d : DistanceMetric object (default = abs(x-y))
        the distance measure used for the dtw
        
    mww : int (default = 10000)
        the size of the Sakoe-Chiba warping window
    
    Returns
    -------
    DTW distance between ts_a and ts_b
    """
    
    # Create cost matrix via broadcasting with large int
    ts_a, ts_b = np.array(ts_a), np.array(ts_b)
    M, N = len(ts_a), len(ts_b)
    cost = np.ones((M, N))
    
    # Fill the first row and column with large int
    cost[0, :] = np.ones((1, N))
    cost[:, 0] = np.ones((M, 1))
    cost[0, 0] = 0
    
    # Fill the rest of the cost matrix within the mww
    for i in range(1, M):
        for j in range(max(1, i - mww), min(N, i + mww)):
            choices = cost[i-1, j-1], cost[i, j-1], cost[i-1, j]
            cost[i, j] = min(choices) + d(ts_a[i], ts_b[j])
            
    # Return DTW distance given the cost matrix
    return cost[-1, -1]

# 示例
ts_a = np.array([1,3,4,2,1,1,2])
ts_b = np.array([1,2,2,4,3,2,1])
print(dtw_distance(ts_a, ts_b))

输出结果：

4.0