[英]How can I calculate the Dynamic Time Warping distance of 2D-Points with Python?
我看过mlpy.dtw_std(x, y, dist_only=True)
但似乎只支持1D-DTW。
我也尝试使用R:
def getDTW(A, B):
""" Calculate the distance of A and B by greedy dynamic time warping.
@param list A list of points
@param list B list of points
@return float Minimal distance you have to move points from A to get B
>>> '%.2f' % greedyMatchingDTW([{'x': 0, 'y': 0}, {'x': 1, 'y': 1}], \
[{'x': 0, 'y': 0}, {'x': 0, 'y': 5}])
'4.12'
>>> '%.2f' % greedyMatchingDTW([{'x': 0, 'y': 0}, {'x':0, 'y': 10}, \
{'x': 1, 'y': 22}, {'x': 2, 'y': 2}], \
[{'x': 0, 'y': 0}, {'x': 0, 'y': 5}])
'30.63'
>>> '%.2f' % greedyMatchingDTW( [{'x': 0, 'y': 0}, {'x': 0, 'y': 5}], \
[{'x': 0, 'y': 0}, {'x':0, 'y': 10}, \
{'x': 1, 'y': 22}, {'x': 2, 'y': 2}])
'30.63'
"""
global logging
import numpy as np
import rpy2.robjects.numpy2ri
from rpy2.robjects.packages import importr
rpy2.robjects.numpy2ri.activate()
# Set up our R namespaces
R = rpy2.robjects.r
DTW = importr('dtw')
An, Bn = [], []
for p in A:
An.append([p['x'], p['y']])
for p in B:
Bn.append([p['x'], p['y']])
alignment = R.dtw(np.array(An), np.array(Bn), keep=True)
dist = alignment.rx('distance')[0][0]
return dist
# I would expect 0 + sqrt(1**2 + (-4)**1) = sqrt(17) = 4.123105625617661
print(getDTW([{'x': 0, 'y': 0}, {'x': 1, 'y': 1}],
[{'x': 0, 'y': 0}, {'x': 0, 'y': 5}]))
# prints 5.53731918799 - why?
但是正如我在底部指出的那样,R并没有给出预期的解决方案。
因此:如何在Python中计算两个二维点列表之间的DTW?
您的期望似乎没有考虑步骤模式。 如果在R中运行以下命令。
library(dtw)
x <- cbind(c(0,1), c(0,1))
y <- cbind(c(0,0), c(0,5))
dtw(x, y, step.pattern=symmetric1)$distance
# [1] 4.123106
您会得到预期的结果。 默认步骤模式为symetric2
dtw(x, y, step.pattern=symmetric2)$distance
# [1] 5.537319
因此,我很确定R正在计算正确的值,只是您的期望可能未与该特定函数的默认值保持一致。
对于第二个示例,symmetric2似乎符合您的期望
x <- cbind(c(0,0,1,2),c(0,10,22,2))
y <- cbind(c(0,0), c(0,5))
dtw(x, y, step.pattern=symmetric2)$distance
# [1] 30.63494
我无法达到您的第三个期望。 我建议您阅读包装文档以了解更多详细信息。
DTW python库之间的比较以及如何使用它们
from cdtw import pydtw
from dtaidistance import dtw
from fastdtw import fastdtw
from scipy.spatial.distance import euclidean
s1=np.array([1,2,3,4],dtype=np.double)
s2=np.array([4,3,2,1],dtype=np.double)
%timeit dtw.distance_fast(s1, s2)
4.1 µs ± 28.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit d2 = pydtw.dtw(s1,s2,pydtw.Settings(step = 'p0sym', window = 'palival', param = 2.0, norm = False, compute_path = True)).get_dist()
45.6 µs ± 3.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit d3,_=fastdtw(s1, s2, dist=euclidean)
901 µs ± 9.95 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
dtaidistance是迄今为止最快的。
这是dtaidistance git:
https://github.com/wannesm/dtaidistance
要安装,只需:
pip install dtaidistance
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