class_docstr: Classification in the tangent space.
  
  Project data in the tangent space and apply a classifier on the projected
  data. This is a simple helper to pipeline the tangent space projection and
  a classifier. Default classifier is LogisticRegression
  
  Parameters
  ----------
  metric : string | dict, default='riemann'
      The type of metric used for reference matrix estimation (see
      `mean_covariance` for the list of supported metric) and for tangent
      space map (see `tangent_space` for the list of supported metric).
      The metric could be a dict with two keys, `mean` and `map` in
      order to pass different metrics for the reference matrix estimation
      and the tangent space mapping.
  tsupdate : bool, default=False
      Activate tangent space update for covariate shift correction between
      training and test, as described in [2]. This is not compatible with
      online implementation. Performance are better when the number of
      matrices for prediction is higher.
  clf : sklearn classifier, default=LogisticRegression()
      The classifier to apply in the tangent space.
  
  Attributes
  ----------
  classes_ : ndarray, shape (n_classes,)
      Labels for each class.
  
  See Also
  --------
  TangentSpace
  
  Notes
  -----
  .. versionadded:: 0.2.4
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docstr: Get the predictions.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of SPD matrices.
  
  Returns
  -------
  pred : ndarray of int, shape (n_matrices,)
      Predictions for each matrix according to the closest centroid.
docstr_tok: ["Get","the","predictions",".","Parameters","--","--","--","--","--","X",":","ndarray",",","shape","(","n_matrices",",","n_channels",",","n_channels",")","Set","of","SPD","matrices",".","Returns","--","--","--","-","pred",":","ndarray","of","int",",","shape","(","n_matrices",",",")","Predictions","for","each","matrix","according","to","the","closest","centroid","."]
code: def predict(self, X):
      """Get the predictions.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_channels)
          Set of SPD matrices.
  
      Returns
      -------
      pred : ndarray of int, shape (n_matrices,)
          Predictions for each matrix according to the closest centroid.
      """
      return self._pipe.predict(X)
  
imports: import functools
  import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
  from sklearn.svm import SVC
  from sklearn.utils.extmath import softmax
  from sklearn.linear_model import LogisticRegression
  from sklearn.pipeline import make_pipeline
  from joblib import Parallel, delayed
  import warnings
  from .utils.kernel import kernel
  from .utils.mean import mean_covariance, mean_power
  from .utils.distance import distance
  from .tangentspace import FGDA, TangentSpace
label_desc: Use image node_id 3 for calling the TSclassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self