class_docstr: Symbolic Fourier Approximation (SFA) Transformer.
  
  Overview: for each series:
      run a sliding window across the series
      for each window
          shorten the series with DFT
          discretise the shortened series into bins set by MFC
          form a word from these discrete values
  by default SFA produces a single word per series (window_size=0)
  if a window is used, it forms a histogram of counts of words.
  
  Parameters
  ----------
  word_length:         int, default = 8
      length of word to shorten window to (using PAA)
  
  alphabet_size:       int, default = 4
      number of values to discretise each value to
  
  window_size:         int, default = 12
      size of window for sliding. Input series
      length for whole series transform
  
  norm:                boolean, default = False
      mean normalise words by dropping first fourier coefficient
  
  binning_method:      {"equi-depth", "equi-width", "information-gain", "kmeans"},
                          default="equi-depth"
      the binning method used to derive the breakpoints.
  
  anova:               boolean, default = False
      If True, the Fourier coefficient selection is done via a one-way
      ANOVA test. If False, the first Fourier coefficients are selected.
      Only applicable if labels are given
  
  bigrams:             boolean, default = False
      whether to create bigrams of SFA words
  
  skip_grams:          boolean, default = False
      whether to create skip-grams of SFA words
  
  remove_repeat_words: boolean, default = False
      whether to use numerosity reduction (default False)
  
  levels:              int, default = 1
      Number of spatial pyramid levels
  
  save_words:          boolean, default = False
      whether to save the words generated for each series (default False)
  
  return_pandas_data_series:          boolean, default = False
      set to true to return Pandas Series as a result of transform.
      setting to true reduces speed significantly but is required for
      automatic test.
  
  n_jobs:              int, optional, default = 1
      The number of jobs to run in parallel for both `transform`.
      ``-1`` means using all processors.
  
  Attributes
  ----------
  words: []
  breakpoints: = []
  num_insts = 0
  num_atts = 0
  
  References
  ----------
  .. [1] Schäfer, Patrick, and Mikael Högqvist. "SFA: a symbolic fourier approximation
  and  index for similarity search in high dimensional datasets." Proceedings of the
  15th international conference on extending database technology. 2012.
code: def _mcb(self, dft):
      num_windows_per_inst = math.ceil(
          self.series_length / self.window_size
      )
      total_num_windows = int(self.n_instances * num_windows_per_inst)
      breakpoints = np.zeros((self.word_length, self.alphabet_size))
  
      for letter in range(self.word_length):
          res = [
              round(dft[i][letter] * 100) / 100
              for i in range(total_num_windows)
          ]
          column = np.sort(np.array(res))
  
          bin_index = 0
  
          # use equi-depth binning
          if self.binning_method == "equi-depth":
              target_bin_depth = total_num_windows / self.alphabet_size
  
              for bp in range(self.alphabet_size - 1):
                  bin_index += target_bin_depth
                  breakpoints[letter][bp] = column[int(bin_index)]
  
          # use equi-width binning aka equi-frequency binning
          elif self.binning_method == "equi-width":
              target_bin_width = (
                  column[-1] - column[0]
              ) / self.alphabet_size
  
              for bp in range(self.alphabet_size - 1):
                  breakpoints[letter][bp] = (
                      bp + 1
                  ) * target_bin_width + column[0]
  
      breakpoints[:, self.alphabet_size - 1] = sys.float_info.max
      return breakpoints
  
imports: import math
  import sys
  import warnings
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn.feature_selection import f_classif
  from sklearn.preprocessing import KBinsDiscretizer
  from sklearn.tree import DecisionTreeClassifier
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation.panel import check_X
label_desc: Use image node_id 7 for calling the SFA obj's underlying member method code with example usage: obj._mcb(dft) and returns: breakpoints