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.
docstr: Return overall distance of alignment.
  
      core logic
  
  Behaviour: returns overall distance corresponding to alignment
      not all aligners will return or implement this (optional)
  Accesses in self:
      Fitted model attributes ending in "_".
  
  Returns
  -------
  distance: float - overall distance between all elements of X passed to fit
code: def _get_distance(self):
      """Return overall distance of alignment.
  
          core logic
  
      Behaviour: returns overall distance corresponding to alignment
          not all aligners will return or implement this (optional)
      Accesses in self:
          Fitted model attributes ending in "_".
  
      Returns
      -------
      distance: float - overall distance between all elements of X passed to fit
      """
      return self.dist_
  
file_path: sktime/sktime/alignment/lucky.py
imports: import numpy
  import pandas
  from sktime.alignment.base import BaseAligner
label_desc: Use image node_id 4 for calling the AlignerLuckyDtw obj's underlying member method code with example usage: obj._get_distance() and returns: self