class_docstr: Symbolic Aggregate approXimation (SAX) transformer.
  
  as described in
  Jessica Lin, Eamonn Keogh, Li Wei and Stefano Lonardi,
  "Experiencing SAX: a novel symbolic representation of time series"
  Data Mining and Knowledge Discovery, 15(2):107-144
  Overview: for each series:
      run a sliding window across the series
      for each window
          shorten the series with PAA (Piecewise Approximate Aggregation)
          discretise the shortened series into fixed bins
          form a word from these discrete values
  by default SAX produces a single word per series (window_size=0).
  SAX returns a pandas data frame where column 0 is the histogram (sparse
  pd.series)
  of each series.
  
  Parameters
  ----------
  word_length:         int, length of word to shorten window to (using
  PAA) (default 8)
  alphabet_size:       int, number of values to discretise each value
  to (default to 4)
  window_size:         int, size of window for sliding. Input series
  length for whole series transform (default to 12)
  remove_repeat_words: boolean, whether to use numerosity reduction (
  default False)
  save_words:          boolean, whether to use numerosity reduction (
  default False)
  
  return_pandas_data_series:          boolean, default = True
      set to true to return Pandas Series as a result of transform.
      setting to true reduces speed significantly but is required for
      automatic test.
  
  Attributes
  ----------
  words:      history = []
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code: def _generate_breakpoints(self):
      # Pre-made gaussian curve breakpoints from UEA TSC codebase
      return {
          2: [0, sys.float_info.max],
          3: [-0.43, 0.43, sys.float_info.max],
          4: [-0.67, 0, 0.67, sys.float_info.max],
          5: [-0.84, -0.25, 0.25, 0.84, sys.float_info.max],
          6: [-0.97, -0.43, 0, 0.43, 0.97, sys.float_info.max],
          7: [
              -1.07,
              -0.57,
              -0.18,
              0.18,
              0.57,
              1.07,
              sys.float_info.max,
          ],
          8: [
              -1.15,
              -0.67,
              -0.32,
              0,
              0.32,
              0.67,
              1.15,
              sys.float_info.max,
          ],
          9: [
              -1.22,
              -0.76,
              -0.43,
              -0.14,
              0.14,
              0.43,
              0.76,
              1.22,
              sys.float_info.max,
          ],
          10: [
              -1.28,
              -0.84,
              -0.52,
              -0.25,
              0.0,
              0.25,
              0.52,
              0.84,
              1.28,
              sys.float_info.max,
          ],
      }[self.alphabet_size]
  
file_path: sktime/sktime/transformations/panel/dictionary_based/_sax.py
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.dictionary_based import PAA
label_desc: Use image node_id 5 for calling the SAX obj's underlying member method code with example usage: obj._generate_breakpoints() and returns: dict