class_docstr: Piecewise Aggregate Approximation (PAA) transformation.
  
  PAA was originally presented in [1]_.
  
  Parameters
  ----------
  n_segments : int (default: 1)
      Number of PAA segments to compute
  
  Notes
  -----
      This method requires a dataset of equal-sized time series.
  
  Examples
  --------
  >>> paa = PiecewiseAggregateApproximation(n_segments=3)
  >>> data = [[-1., 2., 0.1, -1., 1., -1.], [1., 3.2, -1., -3., 1., -1.]]
  >>> paa_data = paa.fit_transform(data)
  >>> paa_data.shape
  (2, 3, 1)
  >>> paa_data
  array([[[ 0.5 ],
          [-0.45],
          [ 0.  ]],
  <BLANKLINE>
         [[ 2.1 ],
          [-2.  ],
          [ 0.  ]]])
  >>> paa.distance_paa(paa_data[0], paa_data[1])  # doctest: +ELLIPSIS
  3.15039...
  >>> paa.distance(data[0], data[1])  # doctest: +ELLIPSIS
  3.15039...
  >>> paa.inverse_transform(paa_data)
  array([[[ 0.5 ],
          [ 0.5 ],
          [-0.45],
          [-0.45],
          [ 0.  ],
          [ 0.  ]],
  <BLANKLINE>
         [[ 2.1 ],
          [ 2.1 ],
          [-2.  ],
          [-2.  ],
          [ 0.  ],
          [ 0.  ]]])
  
  References
  ----------
  .. [1] E. Keogh & M. Pazzani. Scaling up dynamic time warping for
     datamining applications. SIGKDD 2000, pp. 285--289.
docstr: Compute distance between PAA representations as defined in [1]_.
  
  Parameters
  ----------
  paa1 : array-like
      PAA representation of a time series
  paa2 : array-like
      PAA representation of another time series
  
  Returns
  -------
  float
      PAA distance
  
  References
  ----------
  .. [1] E. Keogh & M. Pazzani. Scaling up dynamic time warping for
     datamining applications. SIGKDD 2000, pp. 285--289.
docstr_tok: ["Compute","distance","between","PAA","representations","as","defined","in","[","1","]","_.","Parameters","--","--","--","--","--","paa1",":","array-like","PAA","representation","of","a","time","series","paa2",":","array-like","PAA","representation","of","another","time","series","Returns","--","--","--","-","float","PAA","distance","References","--","--","--","--","--","..","[","1","]","E.","Keogh","&","M.","Pazzani",".","Scaling","up","dynamic","time","warping","for","datamining","applications",".","SIGKDD","2000",",","pp",".","285","--","289","."]
code: def distance_paa(self, paa1, paa2):
      """Compute distance between PAA representations as defined in [1]_.
  
      Parameters
      ----------
      paa1 : array-like
          PAA representation of a time series
      paa2 : array-like
          PAA representation of another time series
  
      Returns
      -------
      float
          PAA distance
  
      References
      ----------
      .. [1] E. Keogh & M. Pazzani. Scaling up dynamic time warping for
         datamining applications. SIGKDD 2000, pp. 285--289.
      """
      self._is_fitted()
      return numpy.linalg.norm(paa1 - paa2) * numpy.sqrt(
          self._X_fit_dims_[1] / self.n_segments
      )
  
imports: import numpy
  from scipy.stats import norm
  from sklearn.base import TransformerMixin
  from sklearn.utils.validation import check_is_fitted, check_array
  from tslearn.bases import BaseModelPackage, TimeSeriesBaseEstimator
  from tslearn.metrics.cysax import cydist_sax, cyslopes, cydist_1d_sax, inv_transform_1d_sax, inv_transform_sax, inv_transform_paa
  from tslearn.utils import ts_size, check_dims
label_desc: Use image node_id 8 for calling the PiecewiseAggregateApproximation obj's underlying member method code with example usage: obj.distance_paa(paa1, paa2) and returns: unknown