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 _binning(self, X, y=None):
      num_windows_per_inst = math.ceil(
          self.series_length / self.window_size
      )
      dft = np.array(
          [
              self._binning_dft(X[i, :], num_windows_per_inst)
              for i in range(self.n_instances)
          ]
      )
      if self.keep_binning_dft:
          self.binning_dft = dft
      dft = dft.reshape(len(X) * num_windows_per_inst, self.dft_length)
  
      if y is not None:
          y = np.repeat(y, num_windows_per_inst)
  
      if self.anova and y is not None:
          non_constant = np.where(
              ~np.isclose(dft.var(axis=0), np.zeros_like(dft.shape[1]))
          )[0]
  
          # select word-length many indices with best f-score
          if self.word_length <= non_constant.size:
              f, _ = f_classif(dft[:, non_constant], y)
              self.support = non_constant[np.argsort(-f)][
                  : self.word_length
              ]
  
          # sort remaining indices
          # self.support = np.sort(self.support)
  
          # select the Fourier coefficients with highest f-score
          dft = dft[:, self.support]
          self.dft_length = np.max(self.support) + 1
          self.dft_length = (
              self.dft_length + self.dft_length % 2
          )  # even
  
      if self.binning_method == "information-gain":
          return self._igb(dft, y)
      elif self.binning_method == "kmeans":
          return self._k_bins_discretizer(dft)
      else:
          return self._mcb(dft)
  
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 5 for calling the SFA obj's underlying member method code with example usage: obj._binning(X, y) and returns: self, self, self