Class RadiusNeighborsClassifierScikitsLearnNode

Classifier implementing a vote among neighbors within a given radius

This node has been automatically generated by wrapping the ``sklearn.neighbors.classification.RadiusNeighborsClassifier`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict the class labels for the provided data

This node has been automatically generated by wrapping the sklearn.neighbors.classification.RadiusNeighborsClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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.

Overrides: ClassifierNode.label

Fit the model using X as training data and y as target values

This node has been automatically generated by wrapping the sklearn.neighbors.classification.RadiusNeighborsClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix, BallTree, KDTree}

Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric='precomputed'.

y : {array-like, sparse matrix}

Target values of shape = [n_samples] or [n_samples, n_outputs]

Overrides: Node.stop_training