class_docstr: Wrapped Gaussian Process.
  
  The implementation is based on the algorithm 4 of [1]_.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  prior : callable
      Associate to each input a manifold valued point.
  
  References
  ----------
  .. [1] Mallasto, A. and Feragen, A. Wrapped gaussian process
      regression on riemannian manifolds. In 2018 IEEE/CVF
      Conference on Computer Vision and Pattern Recognition
class_docstr_tok: ["Wrapped","Gaussian","Process",".","The","implementation","is","based","on","the","algorithm","4","of","[","1","]","_.","Parameters","--","--","--","--","--","space",":","Manifold","Equipped","manifold",".","prior",":","callable","Associate","to","each","input","a","manifold","valued","point",".","References","--","--","--","--","--","..","[","1","]","Mallasto",",","A.","and","Feragen",",","A",".","Wrapped","gaussian","process","regression","on","riemannian","manifolds",".","In","2018","IEEE\/CVF","Conference","on","Computer","Vision","and","Pattern","Recognition"]
docstr: Compute the tangent targets, using the provided prior.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features) or list of object
      Feature vectors or other representations of training data.
  y : array-like of shape (n_samples,) or (n_samples, n_targets)
  or (n_samples, n1_targets, n2_targets) for
  matrix-valued targets.
      Target values. The target must belongs to the manifold space
  
  Returns
  -------
  tangent_y : array-like of shape (n_samples,) or (n_samples, n_targets)
  or (n_samples, n1_targets, n2_targets)
          Target projected on the associated (by the prior) tangent space.
docstr_tok: ["Compute","the","tangent","targets",",","using","the","provided","prior",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","or","list","of","object","Feature","vectors","or","other","representations","of","training","data",".","y",":","array-like","of","shape","(","n_samples",",",")","or","(","n_samples",",","n_targets",")","or","(","n_samples",",","n1_targets",",","n2_targets",")","for","matrix-valued","targets",".","Target","values",".","The","target","must","belongs","to","the","manifold","space","Returns","--","--","--","-","tangent_y",":","array-like","of","shape","(","n_samples",",",")","or","(","n_samples",",","n_targets",")","or","(","n_samples",",","n1_targets",",","n2_targets",")","Target","projected","on","the","associated","(","by","the","prior",")","tangent","space","."]
code: def _get_tangent_targets(self, X, y):
      """Compute the tangent targets, using the provided prior.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features) or list of object
          Feature vectors or other representations of training data.
      y : array-like of shape (n_samples,) or (n_samples, n_targets)
      or (n_samples, n1_targets, n2_targets) for
      matrix-valued targets.
          Target values. The target must belongs to the manifold space
  
      Returns
      -------
      tangent_y : array-like of shape (n_samples,) or (n_samples, n_targets)
      or (n_samples, n1_targets, n2_targets)
              Target projected on the associated (by the prior) tangent space.
      """
      base_points = self.prior(X)
      return self.space.metric.log(y, base_point=base_points)
  
file_path: geomstats/geomstats/learning/wrapped_gaussian_process.py
imports: from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
  from sklearn.gaussian_process import GaussianProcessRegressor
  import geomstats.backend
label_desc: Use image node_id 3 for calling the WrappedGaussianProcess obj's underlying member method code with example usage: obj._get_tangent_targets(X, y) and returns: self