class_docstr: Alignment path based on lucky dynamic time warping distance.
  
  This aligner returns the alignment path produced by the lucky time warping
  distance [1]_.
  Uses Euclidean distance for multivariate data.
  
  Based on code by Krisztian A Buza's research group.
  
  Parameters
  ----------
  window: int, optional (default=None)
      Maximum distance between indices of aligned series, aka warping window.
      If None, defaults to max(len(ts1), len(ts2)), i.e., no warping window.
  
  References
  ----------
  ..[1] Stephan Spiegel, Brijnesh-Johannes Jain, and Sahin Albayrak.
      Fast time series classification under lucky time warping distance.
      Proceedings of the 29th Annual ACM Symposium on Applied Computing. 2014.
code: def __init__(self, window=None):
      self.window = window
  
      super().__init__()
  
file_path: sktime/sktime/alignment/lucky.py
imports: import numpy
  import pandas
  from sktime.alignment.base import BaseAligner
label_desc: Use image node_id 1 to create a new AlignerLuckyDtw object from inherited base classes: BaseAligner with example: obj = AlignerLuckyDtw(window)