class_docstr: WEASEL+MUSE algorithm.
  
  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.
  
  window_sizes : array-like (default = [0.1, 0.3, 0.5, 0.7, 0.9])
      Size of the sliding windows. All the elements must be either integers
      or floats. In the latter case, each element represents the percentage
      of the size of each time series and must be between 0 and 1; the size
      of the sliding windows will be computed as
      ``np.ceil(window_sizes * n_timestamps)``.
  
  window_steps : None or array-like (default = None)
      Step of the sliding windows. If None, each ``window_step`` is equal to
      ``window_size`` so that the windows are non-overlapping. Otherwise, all
      the elements must be either integers or floats. In the latter case,
      each element represents the percentage of the size of each time series
      and must be between 0 and 1; the step of the sliding windows will be
      computed as ``np.ceil(window_steps * 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 = True)
      If True, the first Fourier coefficient (i.e. the sum of the subseries)
      is dropped. Otherwise, it is kept.
  
  norm_mean : bool (default = True)
      If True, center each subseries before scaling.
  
  norm_std : bool (default = True)
      If True, scale each subseries to unit variance.
  
  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
  
  chi2_threshold : int or float (default = 2)
      The threshold used to perform feature selection. Only the words with
      a chi2 statistic above this threshold will be kept.
  
  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 features indices to terms. Each value is a string with
      4 values separated by a whitespace:
  
          - 'o' or 'd': whether the word is extracted from the original \
              time series ('orig') or from the derivates ('diff')
          - int : feature index
          - int : window size
          - str : word
  
  
  References
  ----------
  .. [1] P. Schäfer, and U. Leser, "Multivariate Time Series Classification
         with WEASEL+MUSE". Proceedings of ACM Conference, (2017).
  
  Examples
  --------
  >>> from pyts.datasets import load_basic_motions
  >>> from pyts.multivariate.transformation import WEASELMUSE
  >>> X_train, X_test, y_train, y_test = load_basic_motions(return_X_y=True)
  >>> transformer = WEASELMUSE(sparse=False)
  >>> transformer.fit_transform(X_train, y_train)
  array(...)
code: def __init__(
      self,
      word_size=4,
      n_bins=4,
      window_sizes=[0.1, 0.3, 0.5, 0.7, 0.9],
      window_steps=None,
      anova=False,
      drop_sum=True,
      norm_mean=True,
      norm_std=True,
      strategy="quantile",
      chi2_threshold=2,
      sparse=True,
      alphabet=None,
  ):
      self.word_size = word_size
      self.n_bins = n_bins
      self.window_sizes = window_sizes
      self.window_steps = window_steps
      self.anova = anova
      self.drop_sum = drop_sum
      self.norm_mean = norm_mean
      self.norm_std = norm_std
      self.strategy = strategy
      self.chi2_threshold = chi2_threshold
      self.sparse = sparse
      self.alphabet = alphabet
  
file_path: pyts/pyts/multivariate/transformation/weasel_muse.py
imports: import numpy
  from scipy.sparse import csr_matrix, hstack
  from sklearn.base import BaseEstimator
  from sklearn.base import clone
  from sklearn.utils.validation import check_is_fitted
  from ...base import MultivariateTransformerMixin
  from ...transformation import WEASEL
  from ..utils import check_3d_array
label_desc: Use image node_id 1 to create a new WEASELMUSE object from inherited base classes: BaseEstimator, MultivariateTransformerMixin with example: obj = WEASELMUSE(word_size, n_bins, window_sizes, window_steps, anova, drop_sum, norm_mean, norm_std, strategy, chi2_threshold, sparse, alphabet)