class_docstr: Bag of Symbolic Fourier Approximation Symbols.
  
  For each time series, subseries are extracted using a sliding window.
  Then the subseries are transformed into a word using the Symbolic
  Fourier Approximation (SFA) algorithm. For each time series, the words
  are grouped together and a histogram counting the occurences of each
  word is created.
  
  Parameters
  ----------
  word_size : int (default = 4)
      Size of each word.
  
  n_bins : int (default = 4)
      The number of bins to produce. It must be between 2 and 26.
  
  strategy : str (default = 'quantile')
      Strategy used to define the widths of the bins:
  
      - 'uniform': All bins in each sample have identical widths
      - 'quantile': All bins in each sample have the same number of points
      - 'normal': Bin edges are quantiles from a standard normal distribution
      - 'entropy': Bin edges are computed using information gain
  
  window_size : int or float (default = 10)
      Size of the sliding window. If float, it represents the percentage of
      the size of each time series and must be between 0 and 1. The window
      size will be computed as ``ceil(window_size * n_timestamps)``.
  
  window_step : int or float (default = 1)
      Step of the sliding window. If float, it represents the percentage of
      the size of each time series and must be between 0 and 1. The window
      size will be computed as ``ceil(window_step * n_timestamps)``.
  
  anova : bool (default = False)
      If True, the Fourier coefficient selection is done via a one-way
      ANOVA test. If False, the first Fourier coefficients are selected.
  
  drop_sum : bool (default = False)
      If True, the first Fourier coefficient (i.e. the sum of the subseries)
      is dropped. Otherwise, it is kept.
  
  norm_mean : bool (default = False)
      If True, center each subseries before scaling.
  
  norm_std : bool (default = False)
      If True, scale each subseries to unit variance.
  
  numerosity_reduction : bool (default = True)
      If True, delete sample-wise all but one occurence of back to back
      identical occurences of the same words.
  
  sparse : bool (default = True)
      Return a sparse matrix if True, else return an array.
  
  alphabet : None, 'ordinal' or array-like, shape = (n_bins,)
      Alphabet to use. If None, the first `n_bins` letters of the Latin
      alphabet are used.
  
  Attributes
  ----------
  vocabulary_ : dict
      A mapping of feature indices to terms.
  
  References
  ----------
  .. [1] P. Schäfer, "The BOSS is concerned with time series classification
         in the presence of noise". Data Mining and Knowledge Discovery,
         29(6), 1505-1530 (2015).
  
  Examples
  --------
  >>> from pyts.datasets import load_gunpoint
  >>> from pyts.transformation import BOSS
  >>> X_train, X_test, _, _ = load_gunpoint(return_X_y=True)
  >>> boss = BOSS(word_size=2, n_bins=2, sparse=False)
  >>> boss.fit(X_train)
  BOSS(...)
  >>> sorted(boss.vocabulary_.values())
  ['aa', 'ab', 'ba', 'bb']
  >>> boss.transform(X_test)
  array(...)
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code: def __init__(
      self,
      word_size=4,
      n_bins=4,
      strategy="quantile",
      window_size=10,
      window_step=1,
      anova=False,
      drop_sum=False,
      norm_mean=False,
      norm_std=False,
      numerosity_reduction=True,
      sparse=True,
      alphabet=None,
  ):
      self.word_size = word_size
      self.n_bins = n_bins
      self.strategy = strategy
      self.window_size = window_size
      self.window_step = window_step
      self.anova = anova
      self.drop_sum = drop_sum
      self.norm_mean = norm_mean
      self.norm_std = norm_std
      self.numerosity_reduction = numerosity_reduction
      self.sparse = sparse
      self.alphabet = alphabet
  
file_path: pyts/pyts/transformation/boss.py
imports: import numpy
  from math import ceil
  from scipy.sparse import csr_matrix
  from sklearn.base import BaseEstimator
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.utils.validation import check_array, check_is_fitted
  from sklearn.utils.multiclass import check_classification_targets
  from ..approximation import SymbolicFourierApproximation
  from ..base import UnivariateTransformerMixin
  from ..utils.utils import _windowed_view
label_desc: Use image node_id 1 to create a new BOSS object from inherited base classes: BaseEstimator, UnivariateTransformerMixin with example: obj = BOSS(word_size, n_bins, strategy, window_size, window_step, anova, drop_sum, norm_mean, norm_std, numerosity_reduction, sparse, alphabet)