class_docstr: Base adapter mixin for tslearn distances and kernels.
docstr: Evaluate tslearn pwtrafo on two time series panels.
  
  Vectorizes _eval_tslearn_pwtrafo over the first dimensions.
  
  Parameters
  ----------
  X, X2: 3D np.ndarrays of format (n_instances n_variables, n_timepoints)
      two time series panels to compute the pairwise transform on
  
  Returns
  -------
  2D np.ndarray
      (i, j)-th entry is _eval_tslearn_pwtrafo(self, X1[i], X2[j])
docstr_tok: ["Evaluate","tslearn","pwtrafo","on","two","time","series","panels",".","Vectorizes","_eval_tslearn_pwtrafo","over","the","first","dimensions",".","Parameters","--","--","--","--","--","X",",","X2",":","3D","np.ndarrays","of","format","(","n_instances","n_variables",",","n_timepoints",")","two","time","series","panels","to","compute","the","pairwise","transform","on","Returns","--","--","--","-","2D","np.ndarray","(","i",",","j",")","-th","entry","is","_eval_tslearn_pwtrafo","(","self",",","X1","[","i","]",",","X2","[","j","]",")"]
code: def _eval_tslearn_pwtrafo_vectorized(self, X, X2=None):
      """Evaluate tslearn pwtrafo on two time series panels.
  
      Vectorizes _eval_tslearn_pwtrafo over the first dimensions.
  
      Parameters
      ----------
      X, X2: 3D np.ndarrays of format (n_instances n_variables, n_timepoints)
          two time series panels to compute the pairwise transform on
  
      Returns
      -------
      2D np.ndarray
          (i, j)-th entry is _eval_tslearn_pwtrafo(self, X1[i], X2[j])
      """
      if X2 is None:
          X2 = X
  
      m = len(X)
      n = len(X2)
      res = np.zeros((m, n))
      for i in range(m):
          for j in range(n):
              res[i, j] = self._eval_tslearn_pwtrafo(X[i], X2[j])
      return res
  
imports: import numpy
label_desc: Use image node_id 4 for calling the _TslearnPwTrafoAdapter obj's underlying member method code with example usage: obj._eval_tslearn_pwtrafo_vectorized(X, X2) and returns: res