class_docstr: Random interval segmenter transformer.
  
  Transformer that segments time-series into random intervals with
  random starting points and lengths. Some
  intervals may overlap and may be duplicates.
  
  Parameters
  ----------
  n_intervals : str, int or float
      Number of intervals to generate.
      - If "log", log of m is used where m is length of time series.
      - If "sqrt", sqrt of m is used.
      - If "random", random number of intervals is generated.
      - If int, n_intervals intervals are generated.
      - If float, int(n_intervals * m) is used with n_intervals giving the
      fraction of intervals of the
      time series length.
  
      For all arguments relative to the length of the time series,
      the generated number of intervals is
      always at least 1.
  
      Default is "sqrt".
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
class_docstr_tok: ["Random","interval","segmenter","transformer",".","Transformer","that","segments","time-series","into","random","intervals","with","random","starting","points","and","lengths",".","Some","intervals","may","overlap","and","may","be","duplicates",".","Parameters","--","--","--","--","--","n_intervals",":","str",",","int","or","float","Number","of","intervals","to","generate",".","-","If","``","log","''",",","log","of","m","is","used","where","m","is","length","of","time","series",".","-","If","``","sqrt","''",",","sqrt","of","m","is","used",".","-","If","``","random","''",",","random","number","of","intervals","is","generated",".","-","If","int",",","n_intervals","intervals","are","generated",".","-","If","float",",","int","(","n_intervals","*","m",")","is","used","with","n_intervals","giving","the","fraction","of","intervals","of","the","time","series","length",".","For","all","arguments","relative","to","the","length","of","the","time","series",",","the","generated","number","of","intervals","is","always","at","least","1",".","Default","is","``","sqrt","''",".","random_state",":","int",",","RandomState","instance","or","None",",","optional","(","default=None",")","If","int",",","random_state","is","the","seed","used","by","the","random","number","generator",";","If","RandomState","instance",",","random_state","is","the","random","number","generator",";","If","None",",","the","random","number","generator","is","the","RandomState","instance","used","by","`","np.random","`","."]
code: def __init__(
      self,
      n_intervals="sqrt",
      min_length=None,
      max_length=None,
      random_state=None,
  ):
      self.n_intervals = n_intervals
      self.min_length = min_length
      self.max_length = max_length
      self.random_state = random_state
      super().__init__()
  
      self.interval_segmenter_ = IntervalSegmenter()
  
imports: import math
  import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.datatypes._utilities import get_time_index
  from sktime.transformations._delegate import _DelegatedTransformer
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_window_length
label_desc: Use image node_id 1 to create a new RandomIntervalSegmenter object from inherited base classes: _DelegatedTransformer with example: obj = RandomIntervalSegmenter(n_intervals, min_length, max_length, random_state)