content: """Nearest Neighbor Regression"""
  
  # Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
  #          Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #          Sparseness support by Lars Buitinck
  #          Multi-output support by Arnaud Joly <a.joly@ulg.ac.be>
  #
  # License: BSD 3 clause (C) INRIA, University of Amsterdam
  
  import numpy as np
  
  from .base import _get_weights, _check_weights, NeighborsBase, KNeighborsMixin
  from .base import RadiusNeighborsMixin, SupervisedFloatMixin
  from ..base import RegressorMixin
  from ..utils import check_array
  
  
  class KNeighborsRegressor(NeighborsBase, KNeighborsMixin,
                            SupervisedFloatMixin,
                            RegressorMixin):
      """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:`kneighbors` 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
      """
  
      def __init__(self, n_neighbors=5, weights='uniform',
                   algorithm='auto', leaf_size=30,
                   p=2, metric='minkowski', metric_params=None, n_jobs=1,
                   **kwargs):
          self._init_params(n_neighbors=n_neighbors,
                            algorithm=algorithm,
                            leaf_size=leaf_size, metric=metric, p=p,
                            metric_params=metric_params, n_jobs=n_jobs, **kwargs)
          self.weights = _check_weights(weights)
  
      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
  
  
  class RadiusNeighborsRegressor(NeighborsBase, RadiusNeighborsMixin,
                                 SupervisedFloatMixin,
                                 RegressorMixin):
      """Regression based on neighbors within a fixed radius.
  
      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
      ----------
      radius : float, optional (default = 1.0)
          Range of parameter space to use by default for :meth:`radius_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.
  
      Examples
      --------
      >>> X = [[0], [1], [2], [3]]
      >>> y = [0, 0, 1, 1]
      >>> from sklearn.neighbors import RadiusNeighborsRegressor
      >>> neigh = RadiusNeighborsRegressor(radius=1.0)
      >>> neigh.fit(X, y) # doctest: +ELLIPSIS
      RadiusNeighborsRegressor(...)
      >>> print(neigh.predict([[1.5]]))
      [ 0.5]
  
      See also
      --------
      NearestNeighbors
      KNeighborsRegressor
      KNeighborsClassifier
      RadiusNeighborsClassifier
  
      Notes
      -----
      See :ref:`Nearest Neighbors <neighbors>` in the online documentation
      for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
      https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
      """
  
      def __init__(self, radius=1.0, weights='uniform',
                   algorithm='auto', leaf_size=30,
                   p=2, metric='minkowski', metric_params=None, **kwargs):
          self._init_params(radius=radius,
                            algorithm=algorithm,
                            leaf_size=leaf_size,
                            p=p, metric=metric, metric_params=metric_params,
                            **kwargs)
          self.weights = _check_weights(weights)
  
      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.radius_neighbors(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.array([np.mean(_y[ind, :], axis=0)
                                 for ind in neigh_ind])
          else:
              y_pred = np.array([(np.average(_y[ind, :], axis=0,
                                             weights=weights[i]))
                                 for (i, ind) in enumerate(neigh_ind)])
  
          if self._y.ndim == 1:
              y_pred = y_pred.ravel()
  
          return y_pred
  