Class RadiusNeighborsRegressorScikitsLearnNode

Regression based on neighbors within a fixed radius.

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

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``.

http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

Overrides: object.__init__

Predict the target for the provided data

This node has been automatically generated by wrapping the sklearn.neighbors.regression.RadiusNeighborsRegressor 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 int, shape = [n_samples] or [n_samples, n_outputs]

Target values

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

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

This node has been automatically generated by wrapping the sklearn.neighbors.regression.RadiusNeighborsRegressor 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, array of float values, shape = [n_samples]

or [n_samples, n_outputs]

Overrides: Node.stop_training