content: """Nearest Neighbor Classification"""
  
  # Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
  #          Fabian Pedregosa <fabian.pedregosa@inria.fr>
  #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #          Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
  #          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 scipy import stats
  from ..utils.extmath import weighted_mode
  
  from .base import \
      _check_weights, _get_weights, \
      NeighborsBase, KNeighborsMixin,\
      RadiusNeighborsMixin, SupervisedIntegerMixin
  from ..base import ClassifierMixin
  from ..utils import check_array
  
  
  class KNeighborsClassifier(NeighborsBase, KNeighborsMixin,
                             SupervisedIntegerMixin, ClassifierMixin):
      """Classifier implementing the k-nearest neighbors vote.
  
      Read more in the :ref:`User Guide <classification>`.
  
      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 KNeighborsClassifier
      >>> neigh = KNeighborsClassifier(n_neighbors=3)
      >>> neigh.fit(X, y) # doctest: +ELLIPSIS
      KNeighborsClassifier(...)
      >>> print(neigh.predict([[1.1]]))
      [0]
      >>> print(neigh.predict_proba([[0.9]]))
      [[ 0.66666667  0.33333333]]
  
      See also
      --------
      RadiusNeighborsClassifier
      KNeighborsRegressor
      RadiusNeighborsRegressor
      NearestNeighbors
  
      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.
  
      http://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 class labels 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 shape [n_samples] or [n_samples, n_outputs]
              Class labels for each data sample.
          """
          X = check_array(X, accept_sparse='csr')
  
          neigh_dist, neigh_ind = self.kneighbors(X)
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          n_outputs = len(classes_)
          n_samples = X.shape[0]
          weights = _get_weights(neigh_dist, self.weights)
  
          y_pred = np.empty((n_samples, n_outputs), dtype=classes_[0].dtype)
          for k, classes_k in enumerate(classes_):
              if weights is None:
                  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
              else:
                  mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)
  
              mode = np.asarray(mode.ravel(), dtype=np.intp)
              y_pred[:, k] = classes_k.take(mode)
  
          if not self.outputs_2d_:
              y_pred = y_pred.ravel()
  
          return y_pred
  
      def predict_proba(self, X):
          """Return probability estimates for the test data X.
  
          Parameters
          ----------
          X : array-like, shape (n_query, n_features), \
                  or (n_query, n_indexed) if metric == 'precomputed'
              Test samples.
  
          Returns
          -------
          p : array of shape = [n_samples, n_classes], or a list of n_outputs
              of such arrays if n_outputs > 1.
              The class probabilities of the input samples. Classes are ordered
              by lexicographic order.
          """
          X = check_array(X, accept_sparse='csr')
  
          neigh_dist, neigh_ind = self.kneighbors(X)
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
  
          n_samples = X.shape[0]
  
          weights = _get_weights(neigh_dist, self.weights)
          if weights is None:
              weights = np.ones_like(neigh_ind)
  
          all_rows = np.arange(X.shape[0])
          probabilities = []
          for k, classes_k in enumerate(classes_):
              pred_labels = _y[:, k][neigh_ind]
              proba_k = np.zeros((n_samples, classes_k.size))
  
              # a simple ':' index doesn't work right
              for i, idx in enumerate(pred_labels.T):  # loop is O(n_neighbors)
                  proba_k[all_rows, idx] += weights[:, i]
  
              # normalize 'votes' into real [0,1] probabilities
              normalizer = proba_k.sum(axis=1)[:, np.newaxis]
              normalizer[normalizer == 0.0] = 1.0
              proba_k /= normalizer
  
              probabilities.append(proba_k)
  
          if not self.outputs_2d_:
              probabilities = probabilities[0]
  
          return probabilities
  
  
  class RadiusNeighborsClassifier(NeighborsBase, RadiusNeighborsMixin,
                                  SupervisedIntegerMixin, ClassifierMixin):
      """Classifier implementing a vote among neighbors within a given radius
  
      Read more in the :ref:`User Guide <classification>`.
  
      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.
  
      outlier_label : int, optional (default = None)
          Label, which is given for outlier samples (samples with no
          neighbors on given radius).
          If set to None, ValueError is raised, when outlier is detected.
  
      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 RadiusNeighborsClassifier
      >>> neigh = RadiusNeighborsClassifier(radius=1.0)
      >>> neigh.fit(X, y) # doctest: +ELLIPSIS
      RadiusNeighborsClassifier(...)
      >>> print(neigh.predict([[1.5]]))
      [0]
  
      See also
      --------
      KNeighborsClassifier
      RadiusNeighborsRegressor
      KNeighborsRegressor
      NearestNeighbors
  
      Notes
      -----
      See :ref:`Nearest Neighbors <neighbors>` in the online documentation
      for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
      http://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',
                   outlier_label=None, metric_params=None, **kwargs):
          self._init_params(radius=radius,
                            algorithm=algorithm,
                            leaf_size=leaf_size,
                            metric=metric, p=p, metric_params=metric_params,
                            **kwargs)
          self.weights = _check_weights(weights)
          self.outlier_label = outlier_label
  
      def predict(self, X):
          """Predict the class labels 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 shape [n_samples] or [n_samples, n_outputs]
              Class labels for each data sample.
  
          """
          X = check_array(X, accept_sparse='csr')
          n_samples = X.shape[0]
  
          neigh_dist, neigh_ind = self.radius_neighbors(X)
          inliers = [i for i, nind in enumerate(neigh_ind) if len(nind) != 0]
          outliers = [i for i, nind in enumerate(neigh_ind) if len(nind) == 0]
  
          classes_ = self.classes_
          _y = self._y
          if not self.outputs_2d_:
              _y = self._y.reshape((-1, 1))
              classes_ = [self.classes_]
          n_outputs = len(classes_)
  
          if self.outlier_label is not None:
              neigh_dist[outliers] = 1e-6
          elif outliers:
              raise ValueError('No neighbors found for test samples %r, '
                               'you can try using larger radius, '
                               'give a label for outliers, '
                               'or consider removing them from your dataset.'
                               % outliers)
  
          weights = _get_weights(neigh_dist, self.weights)
  
          y_pred = np.empty((n_samples, n_outputs), dtype=classes_[0].dtype)
          for k, classes_k in enumerate(classes_):
              pred_labels = np.array([_y[ind, k] for ind in neigh_ind],
                                     dtype=object)
              if weights is None:
                  mode = np.array([stats.mode(pl)[0]
                                   for pl in pred_labels[inliers]], dtype=np.int)
              else:
                  mode = np.array([weighted_mode(pl, w)[0]
                                   for (pl, w)
                                   in zip(pred_labels[inliers], weights)],
                                  dtype=np.int)
  
              mode = mode.ravel()
  
              y_pred[inliers, k] = classes_k.take(mode)
  
          if outliers:
              y_pred[outliers, :] = self.outlier_label
  
          if not self.outputs_2d_:
              y_pred = y_pred.ravel()
  
          return y_pred
  