class_docstr: Matrix Profile transformation.
  
  Matrix Profile was originally presented in [1]_.
  
  Parameters
  ----------
  subsequence_length : int (default: 1)
      Length of the subseries (also called window size) to be used for
      subseries distance computations.
  
  implementation : str (default: "numpy")
      Matrix profile implementation to use.
      Defaults to "numpy" to use the pure numpy version.
      All the available implementations are ["numpy", "stump", "gpu_stump"].
  
      "stump" and "gpu_stump" are both implementations from the stumpy
      python library, the latter requiring a GPU.
      Stumpy is a library for efficiently computing the matrix profile which
      is optimized for speed, performance and memory.
      See [2]_ for the documentation.
      "numpy" is the default pure numpy implementation and does not require
      stumpy to be installed.
  
  scale: bool (default: True)
       Whether input data should be scaled for each feature of each time
       series to have zero mean and unit variance.
       Default for this parameter is set to `True` to match the standard
       matrix profile setup.
  
  Examples
  --------
  >>> time_series = [0., 1., 3., 2., 9., 1., 14., 15., 1., 2., 2., 10., 7.]
  >>> ds = [time_series]
  >>> mp = MatrixProfile(subsequence_length=4, scale=False)
  >>> mp.fit_transform(ds)[0, :, 0]  # doctest: +ELLIPSIS
  array([ 6.85...,  1.41...,  6.16...,  7.93..., 11.40...,
         13.56..., 18.  ..., 13.96...,  1.41...,  6.16...])
  
  References
  ----------
  .. [1] C. M. Yeh, Y. Zhu, L. Ulanova, N.Begum et al.
     Matrix Profile I: All Pairs Similarity Joins for Time Series: A
     Unifying View that Includes Motifs, Discords and Shapelets.
     ICDM 2016.
  
  .. [2] STUMPY documentation https://stumpy.readthedocs.io/en/latest/
class_docstr_tok: ["Matrix","Profile","transformation",".","Matrix","Profile","was","originally","presented","in","[","1","]","_.","Parameters","--","--","--","--","--","subsequence_length",":","int","(","default",":","1",")","Length","of","the","subseries","(","also","called","window","size",")","to","be","used","for","subseries","distance","computations",".","implementation",":","str","(","default",":","``","numpy","''",")","Matrix","profile","implementation","to","use",".","Defaults","to","``","numpy","''","to","use","the","pure","numpy","version",".","All","the","available","implementations","are","[","``","numpy","''",",","``","stump","''",",","``","gpu_stump","''","]",".","``","stump","''","and","``","gpu_stump","''","are","both","implementations","from","the","stumpy","python","library",",","the","latter","requiring","a","GPU",".","Stumpy","is","a","library","for","efficiently","computing","the","matrix","profile","which","is","optimized","for","speed",",","performance","and","memory",".","See","[","2","]","_","for","the","documentation",".","``","numpy","''","is","the","default","pure","numpy","implementation","and","does","not","require","stumpy","to","be","installed",".","scale",":","bool","(","default",":","True",")","Whether","input","data","should","be","scaled","for","each","feature","of","each","time","series","to","have","zero","mean","and","unit","variance",".","Default","for","this","parameter","is","set","to","`","True","`","to","match","the","standard","matrix","profile","setup",".","Examples","--","--","--","--",">",">",">","time_series","=","[","0.",",","1.",",","3.",",","2.",",","9.",",","1.",",","14.",",","15.",",","1.",",","2.",",","2.",",","10.",",","7",".","]",">",">",">","ds","=","[","time_series","]",">",">",">","mp","=","MatrixProfile","(","subsequence_length=4",",","scale=False",")",">",">",">","mp.fit_transform","(","ds",")","[","0",",",":",",","0","]","#","doctest",":","+ELLIPSIS","array","(","[","6.85","...",",","1.41","...",",","6.16","...",",","7.93","...",",","11.40","...",",","13.56","...",",","18",".","...",",","13.96","...",",","1.41","...",",","6.16","...","]",")","References","--","--","--","--","--","..","[","1","]","C.","M.","Yeh",",","Y.","Zhu",",","L.","Ulanova",",","N.Begum","et","al",".","Matrix","Profile","I",":","All","Pairs","Similarity","Joins","for","Time","Series",":","A","Unifying","View","that","Includes","Motifs",",","Discords","and","Shapelets",".","ICDM","2016",".","..","[","2","]","STUMPY","documentation","https",":","\/\/stumpy.readthedocs.io\/en\/latest\/"]
docstr: Transform a dataset of time series into its Matrix Profile
   representation.
  
  Parameters
  ----------
  X : array-like of shape (n_ts, sz, d)
      Time series dataset
  
  Returns
  -------
  numpy.ndarray of shape (n_ts, output_size, 1)
      Matrix-Profile-Transformed dataset. `ouput_size` is equal to
      `sz - subsequence_length + 1`
docstr_tok: ["Transform","a","dataset","of","time","series","into","its","Matrix","Profile","representation",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_ts",",","sz",",","d",")","Time","series","dataset","Returns","--","--","--","-","numpy.ndarray","of","shape","(","n_ts",",","output_size",",","1",")","Matrix-Profile-Transformed","dataset",".","`","ouput_size","`","is","equal","to","`","sz","-","subsequence_length","+","1","`"]
code: def transform(self, X, y=None):
      """Transform a dataset of time series into its Matrix Profile
       representation.
  
      Parameters
      ----------
      X : array-like of shape (n_ts, sz, d)
          Time series dataset
  
      Returns
      -------
      numpy.ndarray of shape (n_ts, output_size, 1)
          Matrix-Profile-Transformed dataset. `ouput_size` is equal to
          `sz - subsequence_length + 1`
      """
      self._is_fitted()
      X = check_array(X, allow_nd=True, force_all_finite=False)
      X = check_dims(
          X, X_fit_dims=self._X_fit_dims, check_n_features_only=True
      )
      return self._transform(X, y)
  
imports: import numpy
  from numpy.lib.stride_tricks import as_strided
  from scipy.spatial.distance import pdist, squareform
  from sklearn.base import TransformerMixin
  from sklearn.utils.validation import check_array, check_is_fitted
  from tslearn.bases import BaseModelPackage, TimeSeriesBaseEstimator
  from tslearn.preprocessing import TimeSeriesScalerMeanVariance
  from tslearn.utils import check_dims
label_desc: Use image node_id 6 for calling the MatrixProfile obj's underlying member method code with example usage: obj.transform(X, y) and returns: self