content: """Base and mixin classes for nearest neighbors"""
  # 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 warnings
  from abc import ABCMeta, abstractmethod
  
  import numpy as np
  from scipy.sparse import csr_matrix, issparse
  
  from .ball_tree import BallTree
  from .kd_tree import KDTree
  from ..base import BaseEstimator
  from ..metrics import pairwise_distances
  from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
  from ..utils import check_X_y, check_array
  from ..utils.fixes import argpartition
  from ..utils.validation import DataConversionWarning
  from ..utils.validation import NotFittedError
  from ..externals import six
  
  
  VALID_METRICS = dict(ball_tree=BallTree.valid_metrics,
                       kd_tree=KDTree.valid_metrics,
                       # The following list comes from the
                       # sklearn.metrics.pairwise doc string
                       brute=(list(PAIRWISE_DISTANCE_FUNCTIONS.keys()) +
                              ['braycurtis', 'canberra', 'chebyshev',
                               'correlation', 'cosine', 'dice', 'hamming',
                               'jaccard', 'kulsinski', 'mahalanobis',
                               'matching', 'minkowski', 'rogerstanimoto',
                               'russellrao', 'seuclidean', 'sokalmichener',
                               'sokalsneath', 'sqeuclidean',
                               'yule', 'wminkowski']))
  
  
  VALID_METRICS_SPARSE = dict(ball_tree=[],
                              kd_tree=[],
                              brute=PAIRWISE_DISTANCE_FUNCTIONS.keys())
  
  
  class NeighborsWarning(UserWarning):
      pass
  
  
  # Make sure that NeighborsWarning are displayed more than once
  warnings.simplefilter("always", NeighborsWarning)
  
  
  def _check_weights(weights):
      """Check to make sure weights are valid"""
      if weights in (None, 'uniform', 'distance'):
          return weights
      elif callable(weights):
          return weights
      else:
          raise ValueError("weights not recognized: should be 'uniform', "
                           "'distance', or a callable function")
  
  
  def _get_weights(dist, weights):
      """Get the weights from an array of distances and a parameter ``weights``
  
      Parameters
      ===========
      dist: ndarray
          The input distances
      weights: {'uniform', 'distance' or a callable}
          The kind of weighting used
  
      Returns
      ========
      weights_arr: array of the same shape as ``dist``
          if ``weights == 'uniform'``, then returns None
      """
      if weights in (None, 'uniform'):
          return None
      elif weights == 'distance':
          # if user attempts to classify a point that was zero distance from one
          # or more training points, those training points are weighted as 1.0
          # and the other points as 0.0
          if dist.dtype is np.dtype(object):
              for point_dist_i, point_dist in enumerate(dist):
                  # check if point_dist is iterable
                  # (ex: RadiusNeighborClassifier.predict may set an element of
                  # dist to 1e-6 to represent an 'outlier')
                  if hasattr(point_dist, '__contains__') and 0. in point_dist:
                      dist[point_dist_i] = point_dist == 0.
                  else:
                      dist[point_dist_i] = 1. / point_dist
          else:
              with np.errstate(divide='ignore'):
                  dist = 1. / dist
              inf_mask = np.isinf(dist)
              inf_row = np.any(inf_mask, axis=1)
              dist[inf_row] = inf_mask[inf_row]
          return dist
      elif callable(weights):
          return weights(dist)
      else:
          raise ValueError("weights not recognized: should be 'uniform', "
                           "'distance', or a callable function")
  
  
  class NeighborsBase(six.with_metaclass(ABCMeta, BaseEstimator)):
      """Base class for nearest neighbors estimators."""
  
      @abstractmethod
      def __init__(self):
          pass
  
      def _init_params(self, n_neighbors=None, radius=None,
                       algorithm='auto', leaf_size=30, metric='minkowski',
                       p=2, metric_params=None, **kwargs):
          if kwargs:
              warnings.warn("Passing additional arguments to the metric "
                            "function as **kwargs is deprecated "
                            "and will no longer be supported in 0.18. "
                            "Use metric_params instead.",
                            DeprecationWarning, stacklevel=3)
              if metric_params is None:
                  metric_params = {}
              metric_params.update(kwargs)
  
          self.n_neighbors = n_neighbors
          self.radius = radius
          self.algorithm = algorithm
          self.leaf_size = leaf_size
          self.metric = metric
          self.metric_params = metric_params
          self.p = p
  
          if algorithm not in ['auto', 'brute',
                               'kd_tree', 'ball_tree']:
              raise ValueError("unrecognized algorithm: '%s'" % algorithm)
  
          if algorithm == 'auto':
              alg_check = 'ball_tree'
          else:
              alg_check = algorithm
  
          if callable(metric):
              if algorithm == 'kd_tree':
                  # callable metric is only valid for brute force and ball_tree
                  raise ValueError(
                      "kd_tree algorithm does not support callable metric '%s'"
                      % metric)
          elif metric not in VALID_METRICS[alg_check]:
              raise ValueError("Metric '%s' not valid for algorithm '%s'"
                               % (metric, algorithm))
  
          if self.metric_params is not None and 'p' in self.metric_params:
              warnings.warn("Parameter p is found in metric_params. "
                            "The corresponding parameter from __init__ "
                            "is ignored.", SyntaxWarning, stacklevel=3)
              effective_p = metric_params['p']
          else:
              effective_p = self.p
  
          if self.metric in ['wminkowski', 'minkowski'] and effective_p < 1:
              raise ValueError("p must be greater than one for minkowski metric")
  
          self._fit_X = None
          self._tree = None
          self._fit_method = None
  
      def _fit(self, X):
          if self.metric_params is None:
              self.effective_metric_params_ = {}
          else:
              self.effective_metric_params_ = self.metric_params.copy()
  
          effective_p = self.effective_metric_params_.get('p', self.p)
          if self.metric in ['wminkowski', 'minkowski']:
              self.effective_metric_params_['p'] = effective_p
  
          self.effective_metric_ = self.metric
          # For minkowski distance, use more efficient methods where available
          if self.metric == 'minkowski':
              p = self.effective_metric_params_.pop('p', 2)
              if p < 1:
                  raise ValueError("p must be greater than one "
                                   "for minkowski metric")
              elif p == 1:
                  self.effective_metric_ = 'manhattan'
              elif p == 2:
                  self.effective_metric_ = 'euclidean'
              elif p == np.inf:
                  self.effective_metric_ = 'chebyshev'
              else:
                  self.effective_metric_params_['p'] = p
  
          if isinstance(X, NeighborsBase):
              self._fit_X = X._fit_X
              self._tree = X._tree
              self._fit_method = X._fit_method
              return self
  
          elif isinstance(X, BallTree):
              self._fit_X = X.data
              self._tree = X
              self._fit_method = 'ball_tree'
              return self
  
          elif isinstance(X, KDTree):
              self._fit_X = X.data
              self._tree = X
              self._fit_method = 'kd_tree'
              return self
  
          X = check_array(X, accept_sparse='csr')
  
          n_samples = X.shape[0]
          if n_samples == 0:
              raise ValueError("n_samples must be greater than 0")
  
          if issparse(X):
              if self.algorithm not in ('auto', 'brute'):
                  warnings.warn("cannot use tree with sparse input: "
                                "using brute force")
              if self.effective_metric_ not in VALID_METRICS_SPARSE['brute']:
                  raise ValueError("metric '%s' not valid for sparse input"
                                   % self.effective_metric_)
              self._fit_X = X.copy()
              self._tree = None
              self._fit_method = 'brute'
              return self
  
          self._fit_method = self.algorithm
          self._fit_X = X
  
          if self._fit_method == 'auto':
              # A tree approach is better for small number of neighbors,
              # and KDTree is generally faster when available
              if (self.n_neighbors is None
                      or self.n_neighbors < self._fit_X.shape[0] // 2):
                  if self.effective_metric_ in VALID_METRICS['kd_tree']:
                      self._fit_method = 'kd_tree'
                  else:
                      self._fit_method = 'ball_tree'
              else:
                  self._fit_method = 'brute'
  
          if self._fit_method == 'ball_tree':
              self._tree = BallTree(X, self.leaf_size,
                                    metric=self.effective_metric_,
                                    **self.effective_metric_params_)
          elif self._fit_method == 'kd_tree':
              self._tree = KDTree(X, self.leaf_size,
                                  metric=self.effective_metric_,
                                  **self.effective_metric_params_)
          elif self._fit_method == 'brute':
              self._tree = None
          else:
              raise ValueError("algorithm = '%s' not recognized"
                               % self.algorithm)
          return self
  
  
  class KNeighborsMixin(object):
      """Mixin for k-neighbors searches"""
  
      def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
          """Finds the K-neighbors of a point.
  
          Returns distance
  
          Parameters
          ----------
          X : array-like, last dimension same as that of fit data, optional
              The query point or points.
              If not provided, neighbors of each indexed point are returned.
              In this case, the query point is not considered its own neighbor.
  
          n_neighbors : int
              Number of neighbors to get (default is the value
              passed to the constructor).
  
          return_distance : boolean, optional. Defaults to True.
              If False, distances will not be returned
  
          Returns
          -------
          dist : array
              Array representing the lengths to points, only present if
              return_distance=True
  
          ind : array
              Indices of the nearest points in the population matrix.
  
          Examples
          --------
          In the following example, we construct a NeighborsClassifier
          class from an array representing our data set and ask who's
          the closest point to [1,1,1]
  
          >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
          >>> from sklearn.neighbors import NearestNeighbors
          >>> neigh = NearestNeighbors(n_neighbors=1)
          >>> neigh.fit(samples) # doctest: +ELLIPSIS
          NearestNeighbors(algorithm='auto', leaf_size=30, ...)
          >>> print(neigh.kneighbors([1., 1., 1.])) # doctest: +ELLIPSIS
          (array([[ 0.5]]), array([[2]]...))
  
          As you can see, it returns [[0.5]], and [[2]], which means that the
          element is at distance 0.5 and is the third element of samples
          (indexes start at 0). You can also query for multiple points:
  
          >>> X = [[0., 1., 0.], [1., 0., 1.]]
          >>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS
          array([[1],
                 [2]]...)
  
          """
          if self._fit_method is None:
              raise NotFittedError("Must fit neighbors before querying.")
  
          if n_neighbors is None:
              n_neighbors = self.n_neighbors
  
          if X is not None:
              query_is_train = False
              X = check_array(X, accept_sparse='csr')
          else:
              query_is_train = True
              X = self._fit_X
              # Include an extra neighbor to account for the sample itself being
              # returned, which is removed later
              n_neighbors += 1
  
          train_size = self._fit_X.shape[0]
          if n_neighbors > train_size:
              raise ValueError(
                  "Expected n_neighbors <= n_samples, "
                  " but n_samples = %d, n_neighbors = %d" %
                  (train_size, n_neighbors)
              )
          n_samples, _ = X.shape
          sample_range = np.arange(n_samples)[:, None]
  
          if self._fit_method == 'brute':
              # for efficiency, use squared euclidean distances
              if self.effective_metric_ == 'euclidean':
                  dist = pairwise_distances(X, self._fit_X, 'euclidean',
                                            squared=True)
              else:
                  dist = pairwise_distances(X, self._fit_X,
                                            self.effective_metric_,
                                            **self.effective_metric_params_)
  
              neigh_ind = argpartition(dist, n_neighbors - 1, axis=1)
              neigh_ind = neigh_ind[:, :n_neighbors]
              # argpartition doesn't guarantee sorted order, so we sort again
              neigh_ind = neigh_ind[
                  sample_range, np.argsort(dist[sample_range, neigh_ind])]
  
              if return_distance:
                  if self.effective_metric_ == 'euclidean':
                      result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind
                  else:
                      result = dist[sample_range, neigh_ind], neigh_ind
              else:
                  result = neigh_ind
  
          elif self._fit_method in ['ball_tree', 'kd_tree']:
              if issparse(X):
                  raise ValueError(
                      "%s does not work with sparse matrices. Densify the data, "
                      "or set algorithm='brute'" % self._fit_method)
              result = self._tree.query(X, n_neighbors,
                                        return_distance=return_distance)
          else:
              raise ValueError("internal: _fit_method not recognized")
  
          if not query_is_train:
              return result
  
          else:
              # If the query data is the same as the indexed data, we would like
              # to ignore the first nearest neighbor of every sample, i.e
              # the sample itself.
              if return_distance:
                  dist, neigh_ind = result
              else:
                  neigh_ind = result
  
              sample_mask = neigh_ind != sample_range
  
              # Corner case: When the number of duplicates are more
              # than the number of neighbors, the first NN will not
              # be the sample, but a duplicate.
              # In that case mask the first duplicate.
              dup_gr_nbrs = np.all(sample_mask, axis=1)
              sample_mask[:, 0][dup_gr_nbrs] = False
  
              neigh_ind = np.reshape(
                  neigh_ind[sample_mask], (n_samples, n_neighbors - 1))
  
              if return_distance:
                  dist = np.reshape(
                      dist[sample_mask], (n_samples, n_neighbors - 1))
                  return dist, neigh_ind
              return neigh_ind
  
      def kneighbors_graph(self, X=None, n_neighbors=None,
                           mode='connectivity'):
          """Computes the (weighted) graph of k-Neighbors for points in X
  
          Parameters
          ----------
          X : array-like, last dimension same as that of fit data, optional
              The query point or points.
              If not provided, neighbors of each indexed point are returned.
              In this case, the query point is not considered its own neighbor.
  
          n_neighbors : int
              Number of neighbors for each sample.
              (default is value passed to the constructor).
  
          mode : {'connectivity', 'distance'}, optional
              Type of returned matrix: 'connectivity' will return the
              connectivity matrix with ones and zeros, in 'distance' the
              edges are Euclidean distance between points.
  
          Returns
          -------
          A : sparse matrix in CSR format, shape = [n_samples, n_samples_fit]
              n_samples_fit is the number of samples in the fitted data
              A[i, j] is assigned the weight of edge that connects i to j.
  
          Examples
          --------
          >>> X = [[0], [3], [1]]
          >>> from sklearn.neighbors import NearestNeighbors
          >>> neigh = NearestNeighbors(n_neighbors=2)
          >>> neigh.fit(X) # doctest: +ELLIPSIS
          NearestNeighbors(algorithm='auto', leaf_size=30, ...)
          >>> A = neigh.kneighbors_graph(X)
          >>> A.toarray()
          array([[ 1.,  0.,  1.],
                 [ 0.,  1.,  1.],
                 [ 1.,  0.,  1.]])
  
          See also
          --------
          NearestNeighbors.radius_neighbors_graph
          """
          if n_neighbors is None:
              n_neighbors = self.n_neighbors
  
          # kneighbors does the None handling.
          if X is not None:
              X = check_array(X, accept_sparse='csr')
              n_samples1 = X.shape[0]
          else:
              n_samples1 = self._fit_X.shape[0]
  
          n_samples2 = self._fit_X.shape[0]
          n_nonzero = n_samples1 * n_neighbors
          A_indptr = np.arange(0, n_nonzero + 1, n_neighbors)
  
          # construct CSR matrix representation of the k-NN graph
          if mode == 'connectivity':
              A_data = np.ones(n_samples1 * n_neighbors)
              A_ind = self.kneighbors(X, n_neighbors, return_distance=False)
  
          elif mode == 'distance':
              A_data, A_ind = self.kneighbors(
                  X, n_neighbors, return_distance=True)
              A_data = np.ravel(A_data)
  
          else:
              raise ValueError(
                  'Unsupported mode, must be one of "connectivity" '
                  'or "distance" but got "%s" instead' % mode)
  
          kneighbors_graph = csr_matrix((A_data, A_ind.ravel(), A_indptr),
                                        shape=(n_samples1, n_samples2))
  
          return kneighbors_graph
  
  
  class RadiusNeighborsMixin(object):
      """Mixin for radius-based neighbors searches"""
  
      def radius_neighbors(self, X=None, radius=None, return_distance=True):
          """Finds the neighbors within a given radius of a point or points.
  
          Return the indices and distances of each point from the dataset
          lying in a ball with size ``radius`` around the points of the query
          array. Points lying on the boundary are included in the results.
  
          The result points are *not* necessarily sorted by distance to their
          query point.
  
          Parameters
          ----------
          X : array-like, (n_samples, n_features), optional
              The query point or points.
              If not provided, neighbors of each indexed point are returned.
              In this case, the query point is not considered its own neighbor.
  
          radius : float
              Limiting distance of neighbors to return.
              (default is the value passed to the constructor).
  
          return_distance : boolean, optional. Defaults to True.
              If False, distances will not be returned
  
          Returns
          -------
          dist : array, shape (n_samples,) of arrays
              Array representing the distances to each point, only present if
              return_distance=True. The distance values are computed according
              to the ``metric`` constructor parameter.
  
          ind : array, shape (n_samples,) of arrays
              An array of arrays of indices of the approximate nearest points
              from the population matrix that lie within a ball of size
              ``radius`` around the query points.
  
          Examples
          --------
          In the following example, we construct a NeighborsClassifier
          class from an array representing our data set and ask who's
          the closest point to [1, 1, 1]:
  
          >>> import numpy as np
          >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
          >>> from sklearn.neighbors import NearestNeighbors
          >>> neigh = NearestNeighbors(radius=1.6)
          >>> neigh.fit(samples) # doctest: +ELLIPSIS
          NearestNeighbors(algorithm='auto', leaf_size=30, ...)
          >>> rng = neigh.radius_neighbors([1., 1., 1.])
          >>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS
          [ 1.5  0.5]
          >>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS
          [1 2]
  
          The first array returned contains the distances to all points which
          are closer than 1.6, while the second array returned contains their
          indices.  In general, multiple points can be queried at the same time.
  
          Notes
          -----
          Because the number of neighbors of each point is not necessarily
          equal, the results for multiple query points cannot be fit in a
          standard data array.
          For efficiency, `radius_neighbors` returns arrays of objects, where
          each object is a 1D array of indices or distances.
          """
          if self._fit_method is None:
              raise NotFittedError("Must fit neighbors before querying.")
  
          if X is not None:
              query_is_train = False
              X = check_array(X, accept_sparse='csr')
          else:
              query_is_train = True
              X = self._fit_X
  
          if radius is None:
              radius = self.radius
  
          n_samples = X.shape[0]
          if self._fit_method == 'brute':
              # for efficiency, use squared euclidean distances
              if self.effective_metric_ == 'euclidean':
                  dist = pairwise_distances(X, self._fit_X, 'euclidean',
                                            squared=True)
                  radius *= radius
              else:
                  dist = pairwise_distances(X, self._fit_X,
                                            self.effective_metric_,
                                            **self.effective_metric_params_)
  
              neigh_ind_list = [np.where(d <= radius)[0] for d in dist]
  
              # See https://github.com/numpy/numpy/issues/5456
              # if you want to understand why this is initialized this way.
              neigh_ind = np.empty(n_samples, dtype='object')
              neigh_ind[:] = neigh_ind_list
  
              if return_distance:
                  dist_array = np.empty(n_samples, dtype='object')
                  if self.effective_metric_ == 'euclidean':
                      dist_list = [np.sqrt(d[neigh_ind[i]])
                                   for i, d in enumerate(dist)]
                  else:
                      dist_list = [d[neigh_ind[i]]
                                   for i, d in enumerate(dist)]
                  dist_array[:] = dist_list
  
                  results = dist_array, neigh_ind
              else:
                  results = neigh_ind
  
          elif self._fit_method in ['ball_tree', 'kd_tree']:
              if issparse(X):
                  raise ValueError(
                      "%s does not work with sparse matrices. Densify the data, "
                      "or set algorithm='brute'" % self._fit_method)
              results = self._tree.query_radius(X, radius,
                                                return_distance=return_distance)
              if return_distance:
                  results = results[::-1]
          else:
              raise ValueError("internal: _fit_method not recognized")
  
          if not query_is_train:
              return results
          else:
              # If the query data is the same as the indexed data, we would like
              # to ignore the first nearest neighbor of every sample, i.e
              # the sample itself.
              if return_distance:
                  dist, neigh_ind = results
              else:
                  neigh_ind = results
  
              for ind, ind_neighbor in enumerate(neigh_ind):
                  mask = ind_neighbor != ind
  
                  neigh_ind[ind] = ind_neighbor[mask]
                  if return_distance:
                      dist[ind] = dist[ind][mask]
  
              if return_distance:
                  return dist, neigh_ind
              return neigh_ind
  
      def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'):
          """Computes the (weighted) graph of Neighbors for points in X
  
          Neighborhoods are restricted the points at a distance lower than
          radius.
  
          Parameters
          ----------
          X : array-like, shape = [n_samples, n_features], optional
              The query point or points.
              If not provided, neighbors of each indexed point are returned.
              In this case, the query point is not considered its own neighbor.
  
          radius : float
              Radius of neighborhoods.
              (default is the value passed to the constructor).
  
          mode : {'connectivity', 'distance'}, optional
              Type of returned matrix: 'connectivity' will return the
              connectivity matrix with ones and zeros, in 'distance' the
              edges are Euclidean distance between points.
  
          Returns
          -------
          A : sparse matrix in CSR format, shape = [n_samples, n_samples]
              A[i, j] is assigned the weight of edge that connects i to j.
  
          Examples
          --------
          >>> X = [[0], [3], [1]]
          >>> from sklearn.neighbors import NearestNeighbors
          >>> neigh = NearestNeighbors(radius=1.5)
          >>> neigh.fit(X) # doctest: +ELLIPSIS
          NearestNeighbors(algorithm='auto', leaf_size=30, ...)
          >>> A = neigh.radius_neighbors_graph(X)
          >>> A.toarray()
          array([[ 1.,  0.,  1.],
                 [ 0.,  1.,  0.],
                 [ 1.,  0.,  1.]])
  
          See also
          --------
          kneighbors_graph
          """
          if X is not None:
              X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
  
          n_samples2 = self._fit_X.shape[0]
          if radius is None:
              radius = self.radius
  
          # construct CSR matrix representation of the NN graph
          if mode == 'connectivity':
              A_ind = self.radius_neighbors(X, radius,
                                            return_distance=False)
              A_data = None
          elif mode == 'distance':
              dist, A_ind = self.radius_neighbors(X, radius,
                                                  return_distance=True)
              A_data = np.concatenate(list(dist))
          else:
              raise ValueError(
                  'Unsupported mode, must be one of "connectivity", '
                  'or "distance" but got %s instead' % mode)
  
          n_samples1 = A_ind.shape[0]
          n_neighbors = np.array([len(a) for a in A_ind])
          A_ind = np.concatenate(list(A_ind))
          if A_data is None:
              A_data = np.ones(len(A_ind))
          A_indptr = np.concatenate((np.zeros(1, dtype=int),
                                     np.cumsum(n_neighbors)))
  
          return csr_matrix((A_data, A_ind, A_indptr),
                            shape=(n_samples1, n_samples2))
  
  
  class SupervisedFloatMixin(object):
      def fit(self, X, y):
          """Fit the model using X as training data and y as target values
  
          Parameters
          ----------
          X : {array-like, sparse matrix, BallTree, KDTree}
              Training data. If array or matrix, shape = [n_samples, n_features]
  
          y : {array-like, sparse matrix}
              Target values, array of float values, shape = [n_samples]
               or [n_samples, n_outputs]
          """
          if not isinstance(X, (KDTree, BallTree)):
              X, y = check_X_y(X, y, "csr", multi_output=True)
          self._y = y
          return self._fit(X)
  
  
  class SupervisedIntegerMixin(object):
      def fit(self, X, y):
          """Fit the model using X as training data and y as target values
  
          Parameters
          ----------
          X : {array-like, sparse matrix, BallTree, KDTree}
              Training data. If array or matrix, shape = [n_samples, n_features]
  
          y : {array-like, sparse matrix}
              Target values of shape = [n_samples] or [n_samples, n_outputs]
  
          """
          if not isinstance(X, (KDTree, BallTree)):
              X, y = check_X_y(X, y, "csr", multi_output=True)
  
          if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:
              if y.ndim != 1:
                  warnings.warn("A column-vector y was passed when a 1d array "
                                "was expected. Please change the shape of y to "
                                "(n_samples, ), for example using ravel().",
                                DataConversionWarning, stacklevel=2)
  
              self.outputs_2d_ = False
              y = y.reshape((-1, 1))
          else:
              self.outputs_2d_ = True
  
          self.classes_ = []
          self._y = np.empty(y.shape, dtype=np.int)
          for k in range(self._y.shape[1]):
              classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True)
              self.classes_.append(classes)
  
          if not self.outputs_2d_:
              self.classes_ = self.classes_[0]
              self._y = self._y.ravel()
  
          return self._fit(X)
  
  
  class UnsupervisedMixin(object):
      def fit(self, X, y=None):
          """Fit the model using X as training data
  
          Parameters
          ----------
          X : {array-like, sparse matrix, BallTree, KDTree}
              Training data. If array or matrix, shape = [n_samples, n_features]
          """
          return self._fit(X)
  