Metadata-Version: 1.1
Name: fastdtw
Version: 0.3.4
Summary: Dynamic Time Warping (DTW) algorithm with an O(N) time and memory complexity.
Home-page: https://github.com/slaypni/fastdtw
Author: Kazuaki Tanida
Author-email: UNKNOWN
License: MIT
Description: fastdtw
        -------
        
        Python implementation of `FastDTW
        <http://cs.fit.edu/~pkc/papers/tdm04.pdf>`_ [1]_, which is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal alignments with an O(N) time and memory complexity.
        
        Install
        -------
        
        ::
        
          pip install fastdtw
        
        Example
        -------
        
        ::
          
          import numpy as np
          from scipy.spatial.distance import euclidean
        
          from fastdtw import fastdtw
        
          x = np.array([[1,1], [2,2], [3,3], [4,4], [5,5]])
          y = np.array([[2,2], [3,3], [4,4]])
          distance, path = fastdtw(x, y, dist=euclidean)
          print(distance)
        
        References
        ----------
        
        .. [1] Stan Salvador, and Philip Chan. "FastDTW: Toward accurate dynamic time warping in linear time and space." Intelligent Data Analysis 11.5 (2007): 561-580.
        
Keywords: dtw
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
