class_docstr: Regression based on k-nearest neighbors.
  
  The target is predicted by local interpolation of the targets
  associated of the nearest neighbors in the training set.
  
  Read more in the :ref:`User Guide <regression>`.
  
  Parameters
  ----------
  n_neighbors : int, optional (default = 5)
      Number of neighbors to use by default for :meth:`k_neighbors` queries.
  
  weights : str or callable
      weight function used in prediction.  Possible values:
  
      - 'uniform' : uniform weights.  All points in each neighborhood
        are weighted equally.
      - 'distance' : weight points by the inverse of their distance.
        in this case, closer neighbors of a query point will have a
        greater influence than neighbors which are further away.
      - [callable] : a user-defined function which accepts an
        array of distances, and returns an array of the same shape
        containing the weights.
  
      Uniform weights are used by default.
  
  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
      Algorithm used to compute the nearest neighbors:
  
      - 'ball_tree' will use :class:`BallTree`
      - 'kd_tree' will use :class:`KDtree`
      - 'brute' will use a brute-force search.
      - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
  
      Note: fitting on sparse input will override the setting of
      this parameter, using brute force.
  
  leaf_size : int, optional (default = 30)
      Leaf size passed to BallTree or KDTree.  This can affect the
      speed of the construction and query, as well as the memory
      required to store the tree.  The optimal value depends on the
      nature of the problem.
  
  metric : string or DistanceMetric object (default='minkowski')
      the distance metric to use for the tree.  The default metric is
      minkowski, and with p=2 is equivalent to the standard Euclidean
      metric. See the documentation of the DistanceMetric class for a
      list of available metrics.
  
  p : integer, optional (default = 2)
      Power parameter for the Minkowski metric. When p = 1, this is
      equivalent to using manhattan_distance (l1), and euclidean_distance
      (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
  
  metric_params : dict, optional (default = None)
      Additional keyword arguments for the metric function.
  
  n_jobs : int, optional (default = 1)
      The number of parallel jobs to run for neighbors search.
      If ``-1``, then the number of jobs is set to the number of CPU cores.
      Doesn't affect :meth:`fit` method.
  
  Examples
  --------
  >>> X = [[0], [1], [2], [3]]
  >>> y = [0, 0, 1, 1]
  >>> from sklearn.neighbors import KNeighborsRegressor
  >>> neigh = KNeighborsRegressor(n_neighbors=2)
  >>> neigh.fit(X, y) # doctest: +ELLIPSIS
  KNeighborsRegressor(...)
  >>> print(neigh.predict([[1.5]]))
  [ 0.5]
  
  See also
  --------
  NearestNeighbors
  RadiusNeighborsRegressor
  KNeighborsClassifier
  RadiusNeighborsClassifier
  
  Notes
  -----
  See :ref:`Nearest Neighbors <neighbors>` in the online documentation
  for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
  .. warning::
  
     Regarding the Nearest Neighbors algorithms, if it is found that two
     neighbors, neighbor `k+1` and `k`, have identical distances but
     but different labels, the results will depend on the ordering of the
     training data.
  
  https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
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docstr: Predict the target for the provided data
  
  Parameters
  ----------
  X : array-like, shape (n_query, n_features),                 or (n_query, n_indexed) if metric == 'precomputed'
      Test samples.
  
  Returns
  -------
  y : array of int, shape = [n_samples] or [n_samples, n_outputs]
      Target values
docstr_tok: ["Predict","the","target","for","the","provided","data","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_query",",","n_features",")",",","or","(","n_query",",","n_indexed",")","if","metric","==","'precomputed'","Test","samples",".","Returns","--","--","--","-","y",":","array","of","int",",","shape","=","[","n_samples","]","or","[","n_samples",",","n_outputs","]","Target","values"]
code: def predict(self, X):
      """Predict the target for the provided data
  
          Parameters
          ----------
          X : array-like, shape (n_query, n_features), \
                  or (n_query, n_indexed) if metric == 'precomputed'
              Test samples.
  
          Returns
          -------
          y : array of int, shape = [n_samples] or [n_samples, n_outputs]
              Target values
          """
      X = check_array(X, accept_sparse="csr")
  
      neigh_dist, neigh_ind = self.kneighbors(X)
  
      weights = _get_weights(neigh_dist, self.weights)
  
      _y = self._y
      if _y.ndim == 1:
          _y = _y.reshape((-1, 1))
  
      if weights is None:
          y_pred = np.mean(_y[neigh_ind], axis=1)
      else:
          y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64)
          denom = np.sum(weights, axis=1)
  
          for j in range(_y.shape[1]):
              num = np.sum(_y[neigh_ind, j] * weights, axis=1)
              y_pred[:, j] = num / denom
  
      if self._y.ndim == 1:
          y_pred = y_pred.ravel()
  
      return y_pred
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neighbors/regression.py
imports: import numpy
  from .base import _get_weights, _check_weights, NeighborsBase, KNeighborsMixin
  from .base import RadiusNeighborsMixin, SupervisedFloatMixin
  from ..base import RegressorMixin
  from ..utils import check_array
label_desc: Use image node_id 2 for calling the KNeighborsRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred