class_docstr: Artefact detection with the Riemannian Potato.
  
  The Riemannian Potato [1]_ is a clustering method used to detect artifact
  in EEG signals. The algorithm iteratively estimates the centroid of clean
  signal by rejecting every trial that is too far from it.
  
  Parameters
  ----------
  metric : string, default='riemann'
      The type of metric used for centroid and distance estimation.
  threshold : float, default=3
      Threshold on z-score of distance to reject artifacts. It is the number
      of standard deviations from the mean of distances to the centroid.
  n_iter_max : int, default=100
      The maximum number of iteration to reach convergence.
  pos_label : int, default=1
      The positive label corresponding to clean data.
  neg_label : int, default=0
      The negative label corresponding to artifact data.
  
  Notes
  -----
  .. versionadded:: 0.2.3
  
  Attributes
  ----------
  covmean_ : ndarray, shape (n_channels, n_channels)
      Centroid of potato.
  
  See Also
  --------
  Kmeans
  MDM
  
  References
  ----------
  .. [1] `The Riemannian Potato: an automatic and adaptive artifact detection
      method for online experiments using Riemannian geometry
      <https://hal.archives-ouvertes.fr/hal-00781701>`_
      A. Barachant, A Andreev, and M. Congedo. TOBI Workshop lV, Jan 2013,
      Sion, Switzerland. pp.19-20.
  .. [2] `The Riemannian Potato Field: A Tool for Online Signal Quality Index
      of EEG
      <https://hal.archives-ouvertes.fr/hal-02015909>`_
      Q. Barthélemy, L. Mayaud, D. Ojeda, and M. Congedo. IEEE Transactions
      on Neural Systems and Rehabilitation Engineering, IEEE Institute of
      Electrical and Electronics Engineers, 2019, 27 (2), pp.244-255
docstr: Return probability of belonging to the potato / being clean.
  
  It is the probability to reject the null hypothesis "clean data",
  computing the right-tailed probability from z-score.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of SPD matrices.
  
  Returns
  -------
  proba : ndarray, shape (n_matrices,)
      Matrix is considered as normal/clean for high value of proba.
      It is considered as abnormal/artifacted for low value of proba.
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code: def predict_proba(self, X):
      """Return probability of belonging to the potato / being clean.
  
      It is the probability to reject the null hypothesis "clean data",
      computing the right-tailed probability from z-score.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_channels)
          Set of SPD matrices.
  
      Returns
      -------
      proba : ndarray, shape (n_matrices,)
          Matrix is considered as normal/clean for high value of proba.
          It is considered as abnormal/artifacted for low value of proba.
      """
      z = self.transform(X)
      proba = self._get_proba(z)
      return proba
  
imports: import numpy
  from joblib import Parallel, delayed
  from scipy.stats import norm, chi2
  import sklearn
  from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin, ClusterMixin, clone
  from sklearn.cluster import KMeans
  from .classification import MDM, _check_metric
  from .utils.mean import mean_covariance
  from .utils.geodesic import geodesic
label_desc: Use image node_id 6 for calling the Potato obj's underlying member method code with example usage: obj.predict_proba(X) and returns: proba