class_docstr: HOG1D transform.
  
  This class is to calculate the HOG1D transform of a
  dataframe of time series data. Works by splitting
  the time series num_intervals times, and calculate
  a histogram of gradients within each interval.
  
  Parameters
  ----------
      num_intervals   : int, length of interval.
      num_bins        : int, num bins in the histogram.
      scaling_factor  : float, a constant that is multiplied
                        to modify the distribution.
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docstr: Split a time series into approximately equal intervals.
  
  Adopted from = https://stackoverflow.com/questions/2130016/splitting
                 -a-list-into-n-parts-of-approximately-equal-length
  
  Parameters
  ----------
  X : a numpy array corresponding to the time series being split
      into approx equal length intervals of shape
      [num_intervals,interval_length].
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code: def _split_time_series(self, X):
      """Split a time series into approximately equal intervals.
  
      Adopted from = https://stackoverflow.com/questions/2130016/splitting
                     -a-list-into-n-parts-of-approximately-equal-length
  
      Parameters
      ----------
      X : a numpy array corresponding to the time series being split
          into approx equal length intervals of shape
          [num_intervals,interval_length].
      """
      avg = len(X) / float(self.num_intervals)
      output = []
      beginning = 0.0
  
      while beginning < len(X):
          output.append(X[int(beginning) : int(beginning + avg)])
          beginning += avg
  
      return output
  
file_path: sktime/sktime/transformations/panel/hog1d.py
imports: import math
  import numbers
  import numpy
  import pandas
  from sktime.datatypes._panel._convert import from_nested_to_2d_array
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 5 for calling the HOG1DTransformer obj's underlying member method code with example usage: obj._split_time_series(X) and returns: output